Part Number: AM67A
I am using BeagleY- AI board running Edge AI image by Beagle - 11.00.00.08 https://www.beagleboard.org/distros/beagley-ai-ti-sdk-edge-ai-11-00-00-08-2025-09-06
the models downloaded from download_models.sh script are working well with edgeai-gst-apps/apps_python script.
I created my custom dataset using label studio, exported in coco format and used it for training using modelmaker (r11.0). I was successfully able to train and compile the model ( Tried yolox_s_lite and yolox_tiny) for AM67A SOC but unable to run inference on the board using edgeai-gst-apps/apps_python script.
(py310) yash@train-server2:~/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training$ cat run.log /home/yash/.pyenv/versions/py3102/lib/python3.10/site-packages/onnxscript/converter.py:816: FutureWarning: 'onnxscript.values.Op.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead. param_schemas = callee.param_schemas() /home/yash/.pyenv/versions/py3102/lib/python3.10/site-packages/onnxscript/converter.py:816: FutureWarning: 'onnxscript.values.OnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead. param_schemas = callee.param_schemas() /home/yash/.pyenv/versions/py3102/lib/python3.10/site-packages/mmengine/optim/optimizer/zero_optimizer.py:11: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead. from torch.distributed.optim import \ 12/30 15:52:00 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.19 (main, Dec 29 2025, 07:14:00) [GCC 13.3.0] CUDA available: True MUSA available: False numpy_random_seed: 728274026 GPU 0: NVIDIA L4 CUDA_HOME: /usr NVCC: Cuda compilation tools, release 12.0, V12.0.140 GCC: gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 PyTorch: 2.4.0+cu124 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 12.4 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 - CuDNN 90.1 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, TorchVision: 0.19.0+cu124 OpenCV: 4.11.0 MMEngine: 0.10.7 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 728274026 Distributed launcher: none Distributed training: False GPU number: 1 ------------------------------------------------------------ 12/30 15:52:03 - mmengine - INFO - Config: auto_scale_lr = dict(base_batch_size=64, enable=False) backend_args = None base_lr = 0.001 classes = ('Target', ) convert_to_lite_model = dict(model_surgery=1) custom_hooks = [ dict(num_last_epochs=15, priority=48, type='YOLOXModeSwitchHook'), dict(priority=48, type='SyncNormHook'), dict( ema_type='ExpMomentumEMA', momentum=0.0001, priority=49, type='EMAHook', update_buffers=True), ] data_root = '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset' dataset_type = 'CocoDataset' default_hooks = dict( checkpoint=dict(interval=1, max_keep_ckpts=3, type='CheckpointHook'), logger=dict(interval=50, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(type='DetVisualizationHook')) default_scope = 'mmdet' env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) export_onnx_model = True img_scale = ( 640, 640, ) img_scales = [ ( 640, 640, ), ( 320, 320, ), ( 960, 960, ), ] interval = 1 launcher = 'none' load_from = '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/downloads/pretrained/yolox_tiny_lite/yolox_tiny_lite_416x416_20220217_checkpoint.pth' log_level = 'INFO' log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50) max_epochs = 15 model = dict( backbone=dict( act_cfg=dict(type='ReLU'), deepen_factor=0.33, norm_cfg=dict(eps=0.001, momentum=0.03, type='BN'), out_indices=( 2, 3, 4, ), spp_kernal_sizes=( 5, 9, 13, ), type='CSPDarknet', use_depthwise=False, widen_factor=0.375), bbox_head=dict( act_cfg=dict(type='ReLU'), feat_channels=96, in_channels=96, loss_bbox=dict( eps=1e-16, loss_weight=5.0, mode='square', reduction='sum', type='IoULoss'), loss_cls=dict( loss_weight=1.0, reduction='sum', type='CrossEntropyLoss', use_sigmoid=True), loss_l1=dict(loss_weight=1.0, reduction='sum', type='L1Loss'), loss_obj=dict( loss_weight=1.0, reduction='sum', type='CrossEntropyLoss', use_sigmoid=True), norm_cfg=dict(eps=0.001, momentum=0.03, type='BN'), num_classes=1, stacked_convs=2, strides=( 8, 16, 32, ), type='YOLOXHead', use_depthwise=False), data_preprocessor=dict( batch_augments=[ dict( interval=10, random_size_range=( 320, 640, ), size_divisor=32, type='BatchSyncRandomResize'), ], pad_size_divisor=32, type='DetDataPreprocessor'), neck=dict( act_cfg=dict(type='ReLU'), in_channels=[ 96, 192, 384, ], norm_cfg=dict(eps=0.001, momentum=0.03, type='BN'), num_csp_blocks=1, out_channels=96, type='YOLOXPAFPN', upsample_cfg=dict(mode='nearest', scale_factor=2), use_depthwise=False), test_cfg=dict(nms=dict(iou_threshold=0.65, type='nms'), score_thr=0.01), train_cfg=dict(assigner=dict(center_radius=2.5, type='SimOTAAssigner')), type='YOLOX') num_last_epochs = 15 optim_wrapper = dict( optimizer=dict( lr=0.001, momentum=0.9, nesterov=True, type='SGD', weight_decay=0.0005), paramwise_cfg=dict(bias_decay_mult=0.0, norm_decay_mult=0.0), type='OptimWrapper') param_scheduler = [ dict( begin=0, by_epoch=True, convert_to_iter_based=True, end=1, type='mmdet.QuadraticWarmupLR'), dict( T_max=10, begin=1, by_epoch=True, convert_to_iter_based=True, end=10, eta_min=5e-05, type='CosineAnnealingLR'), dict(begin=10, by_epoch=True, end=15, factor=1, type='ConstantLR'), ] quantization = 0 resume = False test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=8, dataset=dict( ann_file= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset/annotations/instances_val.json', backend_args=None, data_prefix=dict(img='val/'), data_root= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset', metainfo=dict(classes=('Target', )), pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=True, scale=( 416, 416, ), type='Resize'), dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', ), type='PackDetInputs'), ], test_mode=True, type='CocoDataset'), drop_last=False, num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( ann_file= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset/annotations/instances_val.json', backend_args=None, metric='bbox', type='CocoMetric') test_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=True, scale=( 416, 416, ), type='Resize'), dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', ), type='PackDetInputs'), ] train_cfg = dict(max_epochs=15, type='EpochBasedTrainLoop', val_interval=1) train_dataloader = dict( batch_size=8, dataset=dict( dataset=dict( ann_file= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset/annotations/instances_train.json', backend_args=None, data_prefix=dict(img='train/'), data_root= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset', filter_cfg=dict(filter_empty_gt=False, min_size=32), metainfo=dict(classes=('Target', )), pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), ], type='CocoDataset'), pipeline=[ dict(img_scale=( 640, 640, ), pad_val=114.0, type='Mosaic'), dict( border=( -320, -320, ), scaling_ratio_range=( 0.5, 1.5, ), type='RandomAffine'), dict(type='YOLOXHSVRandomAug'), dict(prob=0.5, type='RandomFlip'), dict(keep_ratio=True, scale=( 640, 640, ), type='Resize'), dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), dict( keep_empty=False, min_gt_bbox_wh=( 1, 1, ), type='FilterAnnotations'), dict(type='PackDetInputs'), ], type='MultiImageMixDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_dataset = dict( dataset=dict( ann_file= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset/annotations/instances_train.json', backend_args=None, data_prefix=dict(img='train/'), data_root= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset', filter_cfg=dict(filter_empty_gt=False, min_size=32), metainfo=dict(classes=('Target', )), pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), ], type='CocoDataset'), pipeline=[ dict(img_scale=( 640, 640, ), pad_val=114.0, type='Mosaic'), dict( border=( -320, -320, ), scaling_ratio_range=( 0.1, 2, ), type='RandomAffine'), dict( img_scale=( 640, 640, ), pad_val=114.0, ratio_range=( 0.8, 1.6, ), type='MixUp'), dict(type='YOLOXHSVRandomAug'), dict(prob=0.5, type='RandomFlip'), dict(keep_ratio=True, scale=( 640, 640, ), type='Resize'), dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), dict( keep_empty=False, min_gt_bbox_wh=( 1, 1, ), type='FilterAnnotations'), dict(type='PackDetInputs'), ], type='MultiImageMixDataset') train_pipeline = [ dict(img_scale=( 640, 640, ), pad_val=114.0, type='Mosaic'), dict( border=( -320, -320, ), scaling_ratio_range=( 0.5, 1.5, ), type='RandomAffine'), dict(type='YOLOXHSVRandomAug'), dict(prob=0.5, type='RandomFlip'), dict(keep_ratio=True, scale=( 640, 640, ), type='Resize'), dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), dict(keep_empty=False, min_gt_bbox_wh=( 1, 1, ), type='FilterAnnotations'), dict(type='PackDetInputs'), ] tta_model = dict( tta_cfg=dict(max_per_img=100, nms=dict(iou_threshold=0.65, type='nms')), type='DetTTAModel') tta_pipeline = [ dict(backend_args=None, type='LoadImageFromFile'), dict( transforms=[ [ dict(keep_ratio=True, scale=( 640, 640, ), type='Resize'), dict(keep_ratio=True, scale=( 320, 320, ), type='Resize'), dict(keep_ratio=True, scale=( 960, 960, ), type='Resize'), ], [ dict(prob=1.0, type='RandomFlip'), dict(prob=0.0, type='RandomFlip'), ], [ dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), ], [ dict(type='LoadAnnotations', with_bbox=True), ], [ dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', ), type='PackDetInputs'), ], ], type='TestTimeAug'), ] val_cfg = dict(type='ValLoop') val_dataloader = dict( batch_size=8, dataset=dict( ann_file= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset/annotations/instances_val.json', backend_args=None, data_prefix=dict(img='val/'), data_root= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset', metainfo=dict(classes=('Target', )), pipeline=[ dict(backend_args=None, type='LoadImageFromFile'), dict(keep_ratio=True, scale=( 416, 416, ), type='Resize'), dict( pad_to_square=True, pad_val=dict(img=( 114.0, 114.0, 114.0, )), type='Pad'), dict(type='LoadAnnotations', with_bbox=True), dict( meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', ), type='PackDetInputs'), ], test_mode=True, type='CocoDataset'), drop_last=False, num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( ann_file= '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/dataset/annotations/instances_val.json', backend_args=None, metric='bbox', type='CocoMetric') vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( name='visualizer', type='DetLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ]) work_dir = '/home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training' 12/30 15:52:08 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used. 12/30 15:52:08 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (49 ) EMAHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (48 ) YOLOXModeSwitchHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (48 ) SyncNormHook (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_save_checkpoint: (49 ) EMAHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- /home/yash/TI/edgeai-tensorlab/edgeai-modeloptimization/torchmodelopt/edgeai_torchmodelopt/xmodelopt/surgery/v1/__init__.py:68: UserWarning: WARNING - xmodelopt.v1.surgery can only replace modules. To replace functions or operators, please use the torch.fx based xmodelopt.v2.surgery instead warnings.warn("WARNING - xmodelopt.v1.surgery can only replace modules. To replace functions or operators, please use the torch.fx based xmodelopt.v2.surgery instead") /home/yash/.pyenv/versions/py3102/lib/python3.10/site-packages/mmengine/runner/checkpoint.py:347: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See github.com/.../SECURITY.md for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. checkpoint = torch.load(filename, map_location=map_location) Loads checkpoint by local backend from path: /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/downloads/pretrained/yolox_tiny_lite/yolox_tiny_lite_416x416_20220217_checkpoint.pth The model and loaded state dict do not match exactly size mismatch for bbox_head.multi_level_conv_cls.0.weight: copying a param with shape torch.Size([80, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 96, 1, 1]). size mismatch for bbox_head.multi_level_conv_cls.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). size mismatch for bbox_head.multi_level_conv_cls.1.weight: copying a param with shape torch.Size([80, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 96, 1, 1]). size mismatch for bbox_head.multi_level_conv_cls.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). size mismatch for bbox_head.multi_level_conv_cls.2.weight: copying a param with shape torch.Size([80, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 96, 1, 1]). size mismatch for bbox_head.multi_level_conv_cls.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). unexpected key in source state_dict: ema_backbone_stem_conv_in_conv_weight, ema_backbone_stem_conv_in_bn_weight, ema_backbone_stem_conv_in_bn_bias, ema_backbone_stem_conv_in_bn_running_mean, ema_backbone_stem_conv_in_bn_running_var, ema_backbone_stem_conv_in_bn_num_batches_tracked, ema_backbone_stem_conv_conv_weight, ema_backbone_stem_conv_bn_weight, ema_backbone_stem_conv_bn_bias, ema_backbone_stem_conv_bn_running_mean, ema_backbone_stem_conv_bn_running_var, ema_backbone_stem_conv_bn_num_batches_tracked, ema_backbone_stage1_0_conv_weight, ema_backbone_stage1_0_bn_weight, ema_backbone_stage1_0_bn_bias, ema_backbone_stage1_0_bn_running_mean, ema_backbone_stage1_0_bn_running_var, ema_backbone_stage1_0_bn_num_batches_tracked, ema_backbone_stage1_1_main_conv_conv_weight, ema_backbone_stage1_1_main_conv_bn_weight, ema_backbone_stage1_1_main_conv_bn_bias, ema_backbone_stage1_1_main_conv_bn_running_mean, ema_backbone_stage1_1_main_conv_bn_running_var, ema_backbone_stage1_1_main_conv_bn_num_batches_tracked, ema_backbone_stage1_1_short_conv_conv_weight, ema_backbone_stage1_1_short_conv_bn_weight, ema_backbone_stage1_1_short_conv_bn_bias, ema_backbone_stage1_1_short_conv_bn_running_mean, ema_backbone_stage1_1_short_conv_bn_running_var, ema_backbone_stage1_1_short_conv_bn_num_batches_tracked, ema_backbone_stage1_1_final_conv_conv_weight, ema_backbone_stage1_1_final_conv_bn_weight, ema_backbone_stage1_1_final_conv_bn_bias, ema_backbone_stage1_1_final_conv_bn_running_mean, ema_backbone_stage1_1_final_conv_bn_running_var, ema_backbone_stage1_1_final_conv_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv1_conv_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_bias, ema_backbone_stage1_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv1_bn_running_var, ema_backbone_stage1_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv2_conv_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_bias, ema_backbone_stage1_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv2_bn_running_var, ema_backbone_stage1_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage2_0_conv_weight, ema_backbone_stage2_0_bn_weight, ema_backbone_stage2_0_bn_bias, ema_backbone_stage2_0_bn_running_mean, ema_backbone_stage2_0_bn_running_var, ema_backbone_stage2_0_bn_num_batches_tracked, ema_backbone_stage2_1_main_conv_conv_weight, ema_backbone_stage2_1_main_conv_bn_weight, ema_backbone_stage2_1_main_conv_bn_bias, ema_backbone_stage2_1_main_conv_bn_running_mean, ema_backbone_stage2_1_main_conv_bn_running_var, ema_backbone_stage2_1_main_conv_bn_num_batches_tracked, ema_backbone_stage2_1_short_conv_conv_weight, ema_backbone_stage2_1_short_conv_bn_weight, ema_backbone_stage2_1_short_conv_bn_bias, ema_backbone_stage2_1_short_conv_bn_running_mean, ema_backbone_stage2_1_short_conv_bn_running_var, ema_backbone_stage2_1_short_conv_bn_num_batches_tracked, ema_backbone_stage2_1_final_conv_conv_weight, ema_backbone_stage2_1_final_conv_bn_weight, ema_backbone_stage2_1_final_conv_bn_bias, ema_backbone_stage2_1_final_conv_bn_running_mean, ema_backbone_stage2_1_final_conv_bn_running_var, ema_backbone_stage2_1_final_conv_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv1_conv_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_bias, ema_backbone_stage2_1_blocks_0_conv1_bn_running_mean, 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ema_bbox_head_multi_level_cls_convs_0_1_bn_num_batches_tracked, ema_bbox_head_multi_level_cls_convs_1_0_conv_weight, ema_bbox_head_multi_level_cls_convs_1_0_bn_weight, ema_bbox_head_multi_level_cls_convs_1_0_bn_bias, ema_bbox_head_multi_level_cls_convs_1_0_bn_running_mean, ema_bbox_head_multi_level_cls_convs_1_0_bn_running_var, ema_bbox_head_multi_level_cls_convs_1_0_bn_num_batches_tracked, ema_bbox_head_multi_level_cls_convs_1_1_conv_weight, ema_bbox_head_multi_level_cls_convs_1_1_bn_weight, ema_bbox_head_multi_level_cls_convs_1_1_bn_bias, ema_bbox_head_multi_level_cls_convs_1_1_bn_running_mean, ema_bbox_head_multi_level_cls_convs_1_1_bn_running_var, ema_bbox_head_multi_level_cls_convs_1_1_bn_num_batches_tracked, ema_bbox_head_multi_level_cls_convs_2_0_conv_weight, ema_bbox_head_multi_level_cls_convs_2_0_bn_weight, ema_bbox_head_multi_level_cls_convs_2_0_bn_bias, ema_bbox_head_multi_level_cls_convs_2_0_bn_running_mean, ema_bbox_head_multi_level_cls_convs_2_0_bn_running_var, ema_bbox_head_multi_level_cls_convs_2_0_bn_num_batches_tracked, ema_bbox_head_multi_level_cls_convs_2_1_conv_weight, ema_bbox_head_multi_level_cls_convs_2_1_bn_weight, ema_bbox_head_multi_level_cls_convs_2_1_bn_bias, ema_bbox_head_multi_level_cls_convs_2_1_bn_running_mean, ema_bbox_head_multi_level_cls_convs_2_1_bn_running_var, ema_bbox_head_multi_level_cls_convs_2_1_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_0_0_conv_weight, ema_bbox_head_multi_level_reg_convs_0_0_bn_weight, ema_bbox_head_multi_level_reg_convs_0_0_bn_bias, ema_bbox_head_multi_level_reg_convs_0_0_bn_running_mean, ema_bbox_head_multi_level_reg_convs_0_0_bn_running_var, ema_bbox_head_multi_level_reg_convs_0_0_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_0_1_conv_weight, ema_bbox_head_multi_level_reg_convs_0_1_bn_weight, ema_bbox_head_multi_level_reg_convs_0_1_bn_bias, ema_bbox_head_multi_level_reg_convs_0_1_bn_running_mean, ema_bbox_head_multi_level_reg_convs_0_1_bn_running_var, ema_bbox_head_multi_level_reg_convs_0_1_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_1_0_conv_weight, ema_bbox_head_multi_level_reg_convs_1_0_bn_weight, ema_bbox_head_multi_level_reg_convs_1_0_bn_bias, ema_bbox_head_multi_level_reg_convs_1_0_bn_running_mean, ema_bbox_head_multi_level_reg_convs_1_0_bn_running_var, ema_bbox_head_multi_level_reg_convs_1_0_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_1_1_conv_weight, ema_bbox_head_multi_level_reg_convs_1_1_bn_weight, ema_bbox_head_multi_level_reg_convs_1_1_bn_bias, ema_bbox_head_multi_level_reg_convs_1_1_bn_running_mean, ema_bbox_head_multi_level_reg_convs_1_1_bn_running_var, ema_bbox_head_multi_level_reg_convs_1_1_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_2_0_conv_weight, ema_bbox_head_multi_level_reg_convs_2_0_bn_weight, ema_bbox_head_multi_level_reg_convs_2_0_bn_bias, ema_bbox_head_multi_level_reg_convs_2_0_bn_running_mean, ema_bbox_head_multi_level_reg_convs_2_0_bn_running_var, ema_bbox_head_multi_level_reg_convs_2_0_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_2_1_conv_weight, ema_bbox_head_multi_level_reg_convs_2_1_bn_weight, ema_bbox_head_multi_level_reg_convs_2_1_bn_bias, ema_bbox_head_multi_level_reg_convs_2_1_bn_running_mean, ema_bbox_head_multi_level_reg_convs_2_1_bn_running_var, ema_bbox_head_multi_level_reg_convs_2_1_bn_num_batches_tracked, ema_bbox_head_multi_level_conv_cls_0_weight, ema_bbox_head_multi_level_conv_cls_0_bias, ema_bbox_head_multi_level_conv_cls_1_weight, ema_bbox_head_multi_level_conv_cls_1_bias, ema_bbox_head_multi_level_conv_cls_2_weight, ema_bbox_head_multi_level_conv_cls_2_bias, ema_bbox_head_multi_level_conv_reg_0_weight, ema_bbox_head_multi_level_conv_reg_0_bias, ema_bbox_head_multi_level_conv_reg_1_weight, ema_bbox_head_multi_level_conv_reg_1_bias, ema_bbox_head_multi_level_conv_reg_2_weight, ema_bbox_head_multi_level_conv_reg_2_bias, ema_bbox_head_multi_level_conv_obj_0_weight, ema_bbox_head_multi_level_conv_obj_0_bias, ema_bbox_head_multi_level_conv_obj_1_weight, ema_bbox_head_multi_level_conv_obj_1_bias, ema_bbox_head_multi_level_conv_obj_2_weight, ema_bbox_head_multi_level_conv_obj_2_bias The model and loaded state dict do not match exactly size mismatch for bbox_head.multi_level_conv_cls.0.weight: copying a param with shape torch.Size([80, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 96, 1, 1]). size mismatch for bbox_head.multi_level_conv_cls.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). size mismatch for bbox_head.multi_level_conv_cls.1.weight: copying a param with shape torch.Size([80, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 96, 1, 1]). size mismatch for bbox_head.multi_level_conv_cls.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). size mismatch for bbox_head.multi_level_conv_cls.2.weight: copying a param with shape torch.Size([80, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 96, 1, 1]). size mismatch for bbox_head.multi_level_conv_cls.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). unexpected key in source state_dict: ema_backbone_stem_conv_in_conv_weight, ema_backbone_stem_conv_in_bn_weight, ema_backbone_stem_conv_in_bn_bias, ema_backbone_stem_conv_in_bn_running_mean, ema_backbone_stem_conv_in_bn_running_var, ema_backbone_stem_conv_in_bn_num_batches_tracked, ema_backbone_stem_conv_conv_weight, ema_backbone_stem_conv_bn_weight, ema_backbone_stem_conv_bn_bias, ema_backbone_stem_conv_bn_running_mean, ema_backbone_stem_conv_bn_running_var, ema_backbone_stem_conv_bn_num_batches_tracked, ema_backbone_stage1_0_conv_weight, ema_backbone_stage1_0_bn_weight, ema_backbone_stage1_0_bn_bias, ema_backbone_stage1_0_bn_running_mean, ema_backbone_stage1_0_bn_running_var, ema_backbone_stage1_0_bn_num_batches_tracked, ema_backbone_stage1_1_main_conv_conv_weight, ema_backbone_stage1_1_main_conv_bn_weight, ema_backbone_stage1_1_main_conv_bn_bias, ema_backbone_stage1_1_main_conv_bn_running_mean, ema_backbone_stage1_1_main_conv_bn_running_var, ema_backbone_stage1_1_main_conv_bn_num_batches_tracked, ema_backbone_stage1_1_short_conv_conv_weight, ema_backbone_stage1_1_short_conv_bn_weight, ema_backbone_stage1_1_short_conv_bn_bias, ema_backbone_stage1_1_short_conv_bn_running_mean, ema_backbone_stage1_1_short_conv_bn_running_var, ema_backbone_stage1_1_short_conv_bn_num_batches_tracked, ema_backbone_stage1_1_final_conv_conv_weight, ema_backbone_stage1_1_final_conv_bn_weight, ema_backbone_stage1_1_final_conv_bn_bias, ema_backbone_stage1_1_final_conv_bn_running_mean, ema_backbone_stage1_1_final_conv_bn_running_var, ema_backbone_stage1_1_final_conv_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv1_conv_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_bias, ema_backbone_stage1_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv1_bn_running_var, ema_backbone_stage1_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv2_conv_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_bias, ema_backbone_stage1_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv2_bn_running_var, ema_backbone_stage1_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage2_0_conv_weight, ema_backbone_stage2_0_bn_weight, ema_backbone_stage2_0_bn_bias, ema_backbone_stage2_0_bn_running_mean, ema_backbone_stage2_0_bn_running_var, ema_backbone_stage2_0_bn_num_batches_tracked, ema_backbone_stage2_1_main_conv_conv_weight, ema_backbone_stage2_1_main_conv_bn_weight, ema_backbone_stage2_1_main_conv_bn_bias, ema_backbone_stage2_1_main_conv_bn_running_mean, ema_backbone_stage2_1_main_conv_bn_running_var, ema_backbone_stage2_1_main_conv_bn_num_batches_tracked, ema_backbone_stage2_1_short_conv_conv_weight, ema_backbone_stage2_1_short_conv_bn_weight, ema_backbone_stage2_1_short_conv_bn_bias, ema_backbone_stage2_1_short_conv_bn_running_mean, ema_backbone_stage2_1_short_conv_bn_running_var, ema_backbone_stage2_1_short_conv_bn_num_batches_tracked, ema_backbone_stage2_1_final_conv_conv_weight, ema_backbone_stage2_1_final_conv_bn_weight, ema_backbone_stage2_1_final_conv_bn_bias, ema_backbone_stage2_1_final_conv_bn_running_mean, ema_backbone_stage2_1_final_conv_bn_running_var, ema_backbone_stage2_1_final_conv_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv1_conv_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_bias, ema_backbone_stage2_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_0_conv1_bn_running_var, ema_backbone_stage2_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv2_conv_weight, ema_backbone_stage2_1_blocks_0_conv2_bn_weight, ema_backbone_stage2_1_blocks_0_conv2_bn_bias, ema_backbone_stage2_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_0_conv2_bn_running_var, ema_backbone_stage2_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_1_conv1_conv_weight, ema_backbone_stage2_1_blocks_1_conv1_bn_weight, ema_backbone_stage2_1_blocks_1_conv1_bn_bias, ema_backbone_stage2_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_1_conv1_bn_running_var, ema_backbone_stage2_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_1_conv2_conv_weight, ema_backbone_stage2_1_blocks_1_conv2_bn_weight, ema_backbone_stage2_1_blocks_1_conv2_bn_bias, ema_backbone_stage2_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_1_conv2_bn_running_var, ema_backbone_stage2_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_2_conv1_conv_weight, ema_backbone_stage2_1_blocks_2_conv1_bn_weight, ema_backbone_stage2_1_blocks_2_conv1_bn_bias, ema_backbone_stage2_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_2_conv1_bn_running_var, ema_backbone_stage2_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_2_conv2_conv_weight, ema_backbone_stage2_1_blocks_2_conv2_bn_weight, ema_backbone_stage2_1_blocks_2_conv2_bn_bias, ema_backbone_stage2_1_blocks_2_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_2_conv2_bn_running_var, ema_backbone_stage2_1_blocks_2_conv2_bn_num_batches_tracked, ema_backbone_stage3_0_conv_weight, ema_backbone_stage3_0_bn_weight, ema_backbone_stage3_0_bn_bias, ema_backbone_stage3_0_bn_running_mean, ema_backbone_stage3_0_bn_running_var, ema_backbone_stage3_0_bn_num_batches_tracked, ema_backbone_stage3_1_main_conv_conv_weight, ema_backbone_stage3_1_main_conv_bn_weight, ema_backbone_stage3_1_main_conv_bn_bias, ema_backbone_stage3_1_main_conv_bn_running_mean, ema_backbone_stage3_1_main_conv_bn_running_var, ema_backbone_stage3_1_main_conv_bn_num_batches_tracked, ema_backbone_stage3_1_short_conv_conv_weight, ema_backbone_stage3_1_short_conv_bn_weight, ema_backbone_stage3_1_short_conv_bn_bias, ema_backbone_stage3_1_short_conv_bn_running_mean, ema_backbone_stage3_1_short_conv_bn_running_var, ema_backbone_stage3_1_short_conv_bn_num_batches_tracked, ema_backbone_stage3_1_final_conv_conv_weight, ema_backbone_stage3_1_final_conv_bn_weight, ema_backbone_stage3_1_final_conv_bn_bias, ema_backbone_stage3_1_final_conv_bn_running_mean, ema_backbone_stage3_1_final_conv_bn_running_var, ema_backbone_stage3_1_final_conv_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_0_conv1_conv_weight, ema_backbone_stage3_1_blocks_0_conv1_bn_weight, ema_backbone_stage3_1_blocks_0_conv1_bn_bias, ema_backbone_stage3_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_0_conv1_bn_running_var, ema_backbone_stage3_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_0_conv2_conv_weight, ema_backbone_stage3_1_blocks_0_conv2_bn_weight, ema_backbone_stage3_1_blocks_0_conv2_bn_bias, ema_backbone_stage3_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_0_conv2_bn_running_var, ema_backbone_stage3_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_1_conv1_conv_weight, ema_backbone_stage3_1_blocks_1_conv1_bn_weight, ema_backbone_stage3_1_blocks_1_conv1_bn_bias, ema_backbone_stage3_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_1_conv1_bn_running_var, ema_backbone_stage3_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_1_conv2_conv_weight, ema_backbone_stage3_1_blocks_1_conv2_bn_weight, ema_backbone_stage3_1_blocks_1_conv2_bn_bias, ema_backbone_stage3_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_1_conv2_bn_running_var, ema_backbone_stage3_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_2_conv1_conv_weight, ema_backbone_stage3_1_blocks_2_conv1_bn_weight, ema_backbone_stage3_1_blocks_2_conv1_bn_bias, ema_backbone_stage3_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_2_conv1_bn_running_var, ema_backbone_stage3_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_2_conv2_conv_weight, ema_backbone_stage3_1_blocks_2_conv2_bn_weight, ema_backbone_stage3_1_blocks_2_conv2_bn_bias, ema_backbone_stage3_1_blocks_2_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_2_conv2_bn_running_var, ema_backbone_stage3_1_blocks_2_conv2_bn_num_batches_tracked, ema_backbone_stage4_0_conv_weight, ema_backbone_stage4_0_bn_weight, ema_backbone_stage4_0_bn_bias, ema_backbone_stage4_0_bn_running_mean, ema_backbone_stage4_0_bn_running_var, ema_backbone_stage4_0_bn_num_batches_tracked, ema_backbone_stage4_1_conv1_conv_weight, ema_backbone_stage4_1_conv1_bn_weight, ema_backbone_stage4_1_conv1_bn_bias, ema_backbone_stage4_1_conv1_bn_running_mean, 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ema_bbox_head_multi_level_conv_obj_1_bias, ema_bbox_head_multi_level_conv_obj_2_weight, ema_bbox_head_multi_level_conv_obj_2_bias 12/30 15:52:15 - mmengine - INFO - Load checkpoint from /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/downloads/pretrained/yolox_tiny_lite/yolox_tiny_lite_416x416_20220217_checkpoint.pth model optimization done loading annotations into memory... Done (t=0.02s) creating index... index created! 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stem.conv_in.bn.weight:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stem.conv_in.bn.bias:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stem.conv.bn.weight:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stem.conv.bn.bias:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stage1.0.bn.weight:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stage1.0.bn.bias:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stage1.1.main_conv.bn.weight:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stage1.1.main_conv.bn.bias:weight_decay=0.0 12/30 15:52:16 - mmengine - INFO - paramwise_options -- backbone.stage1.1.short_conv.bn.weight:weight_decay=0.0 12/30 15:52:16 - 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Done (t=0.00s) creating index... index created! loading annotations into memory... Done (t=0.00s) creating index... index created! 12/30 15:52:19 - mmengine - WARNING - init_weights of YOLOX has been called more than once. 12/30 15:52:19 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in mmengine.readthedocs.io/.../fileio.html 12/30 15:52:19 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 12/30 15:52:19 - mmengine - INFO - Checkpoints will be saved to /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training. 12/30 15:52:19 - mmengine - INFO - No mosaic and mixup aug now! 12/30 15:52:19 - mmengine - INFO - Add additional L1 loss now! /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): /home/yash/.pyenv/versions/py3102/lib/python3.10/site-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:52:35 - mmengine - INFO - Epoch(train) [1][ 50/370] base_lr: 1.8262e-05 lr: 1.8262e-05 eta: 0:27:37 time: 0.3013 data_time: 0.0490 memory: 1905 loss: 9.5077 loss_cls: 2.2473 loss_bbox: 2.7425 loss_obj: 3.5755 loss_l1: 0.9424 12/30 15:52:48 - mmengine - INFO - Epoch(train) [1][100/370] base_lr: 7.3046e-05 lr: 7.3046e-05 eta: 0:25:37 time: 0.2629 data_time: 0.0285 memory: 1560 loss: 5.5345 loss_cls: 0.7326 loss_bbox: 2.4468 loss_obj: 1.6734 loss_l1: 0.6818 12/30 15:53:01 - mmengine - INFO - Epoch(train) [1][150/370] base_lr: 1.6435e-04 lr: 1.6435e-04 eta: 0:24:40 time: 0.2583 data_time: 0.0300 memory: 1905 loss: 4.7564 loss_cls: 0.6249 loss_bbox: 2.3825 loss_obj: 1.1602 loss_l1: 0.5888 12/30 15:53:14 - mmengine - INFO - Epoch(train) [1][200/370] base_lr: 2.9218e-04 lr: 2.9218e-04 eta: 0:24:06 time: 0.2592 data_time: 0.0281 memory: 1269 loss: 4.7406 loss_cls: 0.6202 loss_bbox: 2.4381 loss_obj: 1.1206 loss_l1: 0.5616 12/30 15:53:27 - mmengine - INFO - Epoch(train) [1][250/370] base_lr: 4.5654e-04 lr: 4.5654e-04 eta: 0:23:41 time: 0.2589 data_time: 0.0279 memory: 1753 loss: 4.7457 loss_cls: 0.6259 loss_bbox: 2.4563 loss_obj: 1.0610 loss_l1: 0.6025 12/30 15:53:39 - mmengine - INFO - Epoch(train) [1][300/370] base_lr: 6.5741e-04 lr: 6.5741e-04 eta: 0:23:16 time: 0.2548 data_time: 0.0287 memory: 1269 loss: 4.8824 loss_cls: 0.6271 loss_bbox: 2.5107 loss_obj: 1.1507 loss_l1: 0.5939 12/30 15:53:52 - mmengine - INFO - Epoch(train) [1][350/370] base_lr: 8.9481e-04 lr: 8.9481e-04 eta: 0:22:55 time: 0.2555 data_time: 0.0265 memory: 1905 loss: 4.6379 loss_cls: 0.6093 loss_bbox: 2.4135 loss_obj: 1.0024 loss_l1: 0.6127 12/30 15:53:56 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 15:53:56 - mmengine - INFO - Saving checkpoint at 1 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:54:05 - mmengine - INFO - Epoch(val) [1][50/97] eta: 0:00:03 time: 0.0843 data_time: 0.0283 memory: 1269 12/30 15:54:08 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.01s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.42s). Accumulating evaluation results... DONE (t=0.16s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.700 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.196 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.058 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.384 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.384 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.384 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.555 12/30 15:54:09 - mmengine - INFO - bbox_mAP_copypaste: 0.277 0.700 0.196 0.058 0.218 0.463 12/30 15:54:09 - mmengine - INFO - Epoch(val) [1][97/97] coco/bbox_mAP: 0.2770 coco/bbox_mAP_50: 0.7000 coco/bbox_mAP_75: 0.1960 coco/bbox_mAP_s: 0.0580 coco/bbox_mAP_m: 0.2180 coco/bbox_mAP_l: 0.4630 data_time: 0.0238 time: 0.0781 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:54:24 - mmengine - INFO - Epoch(train) [2][ 50/370] base_lr: 9.9959e-04 lr: 9.9959e-04 eta: 0:22:45 time: 0.2963 data_time: 0.0484 memory: 1560 loss: 5.0500 loss_cls: 0.6286 loss_bbox: 2.6180 loss_obj: 1.1536 loss_l1: 0.6498 12/30 15:54:37 - mmengine - INFO - Epoch(train) [2][100/370] base_lr: 9.9832e-04 lr: 9.9832e-04 eta: 0:22:32 time: 0.2673 data_time: 0.0269 memory: 1905 loss: 4.9227 loss_cls: 0.6241 loss_bbox: 2.5695 loss_obj: 1.0865 loss_l1: 0.6427 12/30 15:54:50 - mmengine - INFO - Epoch(train) [2][150/370] base_lr: 9.9620e-04 lr: 9.9620e-04 eta: 0:22:13 time: 0.2540 data_time: 0.0269 memory: 1408 loss: 4.8399 loss_cls: 0.5983 loss_bbox: 2.5034 loss_obj: 1.1629 loss_l1: 0.5753 12/30 15:55:02 - mmengine - INFO - Epoch(train) [2][200/370] base_lr: 9.9324e-04 lr: 9.9324e-04 eta: 0:21:55 time: 0.2550 data_time: 0.0274 memory: 1408 loss: 4.4809 loss_cls: 0.5916 loss_bbox: 2.4582 loss_obj: 0.8382 loss_l1: 0.5929 12/30 15:55:15 - mmengine - INFO - Epoch(train) [2][250/370] base_lr: 9.8942e-04 lr: 9.8942e-04 eta: 0:21:36 time: 0.2506 data_time: 0.0256 memory: 1905 loss: 4.5811 loss_cls: 0.5870 loss_bbox: 2.4592 loss_obj: 0.9605 loss_l1: 0.5743 12/30 15:55:28 - mmengine - INFO - Epoch(train) [2][300/370] base_lr: 9.8477e-04 lr: 9.8477e-04 eta: 0:21:21 time: 0.2579 data_time: 0.0290 memory: 1905 loss: 4.3163 loss_cls: 0.5818 loss_bbox: 2.3667 loss_obj: 0.8086 loss_l1: 0.5592 12/30 15:55:40 - mmengine - INFO - Epoch(train) [2][350/370] base_lr: 9.7930e-04 lr: 9.7930e-04 eta: 0:21:04 time: 0.2502 data_time: 0.0267 memory: 1120 loss: 4.4707 loss_cls: 0.5980 loss_bbox: 2.4718 loss_obj: 0.8498 loss_l1: 0.5511 12/30 15:55:45 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 15:55:45 - mmengine - INFO - Saving checkpoint at 2 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:55:53 - mmengine - INFO - Epoch(val) [2][50/97] eta: 0:00:03 time: 0.0794 data_time: 0.0243 memory: 1120 12/30 15:55:56 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.29s). Accumulating evaluation results... DONE (t=0.10s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.791 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.106 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.306 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.536 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.606 12/30 15:55:56 - mmengine - INFO - bbox_mAP_copypaste: 0.353 0.791 0.264 0.106 0.306 0.536 12/30 15:55:56 - mmengine - INFO - Epoch(val) [2][97/97] coco/bbox_mAP: 0.3530 coco/bbox_mAP_50: 0.7910 coco/bbox_mAP_75: 0.2640 coco/bbox_mAP_s: 0.1060 coco/bbox_mAP_m: 0.3060 coco/bbox_mAP_l: 0.5360 data_time: 0.0207 time: 0.0735 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:56:10 - mmengine - INFO - Epoch(train) [3][ 50/370] base_lr: 9.7026e-04 lr: 9.7026e-04 eta: 0:20:47 time: 0.2813 data_time: 0.0454 memory: 1905 loss: 4.3365 loss_cls: 0.5860 loss_bbox: 2.4357 loss_obj: 0.7293 loss_l1: 0.5855 12/30 15:56:23 - mmengine - INFO - Epoch(train) [3][100/370] base_lr: 9.6283e-04 lr: 9.6283e-04 eta: 0:20:31 time: 0.2530 data_time: 0.0269 memory: 1560 loss: 4.0819 loss_cls: 0.5684 loss_bbox: 2.3040 loss_obj: 0.6969 loss_l1: 0.5125 12/30 15:56:36 - mmengine - INFO - Epoch(train) [3][150/370] base_lr: 9.5462e-04 lr: 9.5462e-04 eta: 0:20:16 time: 0.2525 data_time: 0.0274 memory: 1753 loss: 4.0948 loss_cls: 0.5711 loss_bbox: 2.3230 loss_obj: 0.6744 loss_l1: 0.5263 12/30 15:56:48 - mmengine - INFO - Epoch(train) [3][200/370] base_lr: 9.4563e-04 lr: 9.4563e-04 eta: 0:20:01 time: 0.2559 data_time: 0.0266 memory: 1753 loss: 4.0330 loss_cls: 0.5704 loss_bbox: 2.2642 loss_obj: 0.6489 loss_l1: 0.5495 12/30 15:57:01 - mmengine - INFO - Epoch(train) [3][250/370] base_lr: 9.3589e-04 lr: 9.3589e-04 eta: 0:19:46 time: 0.2502 data_time: 0.0272 memory: 1269 loss: 4.2132 loss_cls: 0.5791 loss_bbox: 2.3819 loss_obj: 0.7489 loss_l1: 0.5033 12/30 15:57:03 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 15:57:13 - mmengine - INFO - Epoch(train) [3][300/370] base_lr: 9.2541e-04 lr: 9.2541e-04 eta: 0:19:31 time: 0.2502 data_time: 0.0279 memory: 1001 loss: 4.2586 loss_cls: 0.5827 loss_bbox: 2.4244 loss_obj: 0.7362 loss_l1: 0.5152 12/30 15:57:26 - mmengine - INFO - Epoch(train) [3][350/370] base_lr: 9.1420e-04 lr: 9.1420e-04 eta: 0:19:16 time: 0.2521 data_time: 0.0260 memory: 1753 loss: 4.1910 loss_cls: 0.5767 loss_bbox: 2.3596 loss_obj: 0.7487 loss_l1: 0.5059 12/30 15:57:30 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 15:57:30 - mmengine - INFO - Saving checkpoint at 3 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:57:38 - mmengine - INFO - Epoch(val) [3][50/97] eta: 0:00:03 time: 0.0793 data_time: 0.0238 memory: 1560 12/30 15:57:41 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.29s). Accumulating evaluation results... DONE (t=0.10s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.390 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.839 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.302 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.118 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.306 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.583 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.404 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.636 12/30 15:57:42 - mmengine - INFO - bbox_mAP_copypaste: 0.390 0.839 0.302 0.118 0.306 0.583 12/30 15:57:42 - mmengine - INFO - Epoch(val) [3][97/97] coco/bbox_mAP: 0.3900 coco/bbox_mAP_50: 0.8390 coco/bbox_mAP_75: 0.3020 coco/bbox_mAP_s: 0.1180 coco/bbox_mAP_m: 0.3060 coco/bbox_mAP_l: 0.5830 data_time: 0.0202 time: 0.0730 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:57:56 - mmengine - INFO - Epoch(train) [4][ 50/370] base_lr: 8.9734e-04 lr: 8.9734e-04 eta: 0:18:59 time: 0.2805 data_time: 0.0454 memory: 1408 loss: 4.1784 loss_cls: 0.5831 loss_bbox: 2.3898 loss_obj: 0.6741 loss_l1: 0.5314 12/30 15:58:08 - mmengine - INFO - Epoch(train) [4][100/370] base_lr: 8.8448e-04 lr: 8.8448e-04 eta: 0:18:44 time: 0.2507 data_time: 0.0258 memory: 1753 loss: 3.9480 loss_cls: 0.5590 loss_bbox: 2.2407 loss_obj: 0.6471 loss_l1: 0.5012 12/30 15:58:21 - mmengine - INFO - Epoch(train) [4][150/370] base_lr: 8.7098e-04 lr: 8.7098e-04 eta: 0:18:30 time: 0.2530 data_time: 0.0269 memory: 1905 loss: 4.1051 loss_cls: 0.5800 loss_bbox: 2.3457 loss_obj: 0.6682 loss_l1: 0.5111 12/30 15:58:34 - mmengine - INFO - Epoch(train) [4][200/370] base_lr: 8.5686e-04 lr: 8.5686e-04 eta: 0:18:16 time: 0.2547 data_time: 0.0272 memory: 1001 loss: 3.9026 loss_cls: 0.5615 loss_bbox: 2.2448 loss_obj: 0.6304 loss_l1: 0.4659 12/30 15:58:46 - mmengine - INFO - Epoch(train) [4][250/370] base_lr: 8.4214e-04 lr: 8.4214e-04 eta: 0:18:03 time: 0.2545 data_time: 0.0279 memory: 1120 loss: 3.8352 loss_cls: 0.5485 loss_bbox: 2.1934 loss_obj: 0.6257 loss_l1: 0.4677 12/30 15:58:59 - mmengine - INFO - Epoch(train) [4][300/370] base_lr: 8.2684e-04 lr: 8.2684e-04 eta: 0:17:49 time: 0.2556 data_time: 0.0275 memory: 1905 loss: 3.8377 loss_cls: 0.5570 loss_bbox: 2.1937 loss_obj: 0.5707 loss_l1: 0.5163 12/30 15:59:12 - mmengine - INFO - Epoch(train) [4][350/370] base_lr: 8.1101e-04 lr: 8.1101e-04 eta: 0:17:36 time: 0.2525 data_time: 0.0266 memory: 1905 loss: 3.9893 loss_cls: 0.5693 loss_bbox: 2.2902 loss_obj: 0.6207 loss_l1: 0.5091 12/30 15:59:16 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 15:59:16 - mmengine - INFO - Saving checkpoint at 4 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:59:24 - mmengine - INFO - Epoch(val) [4][50/97] eta: 0:00:03 time: 0.0786 data_time: 0.0244 memory: 1269 12/30 15:59:27 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.27s). Accumulating evaluation results... DONE (t=0.09s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.771 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.312 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.252 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.664 12/30 15:59:28 - mmengine - INFO - bbox_mAP_copypaste: 0.360 0.771 0.312 0.076 0.252 0.606 12/30 15:59:28 - mmengine - INFO - Epoch(val) [4][97/97] coco/bbox_mAP: 0.3600 coco/bbox_mAP_50: 0.7710 coco/bbox_mAP_75: 0.3120 coco/bbox_mAP_s: 0.0760 coco/bbox_mAP_m: 0.2520 coco/bbox_mAP_l: 0.6060 data_time: 0.0205 time: 0.0733 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 15:59:42 - mmengine - INFO - Epoch(train) [5][ 50/370] base_lr: 7.8797e-04 lr: 7.8797e-04 eta: 0:17:19 time: 0.2816 data_time: 0.0461 memory: 1905 loss: 3.8804 loss_cls: 0.5561 loss_bbox: 2.2246 loss_obj: 0.6042 loss_l1: 0.4955 12/30 15:59:55 - mmengine - INFO - Epoch(train) [5][100/370] base_lr: 7.7095e-04 lr: 7.7095e-04 eta: 0:17:06 time: 0.2578 data_time: 0.0266 memory: 1753 loss: 3.7749 loss_cls: 0.5515 loss_bbox: 2.1769 loss_obj: 0.5831 loss_l1: 0.4634 12/30 16:00:07 - mmengine - INFO - Epoch(train) [5][150/370] base_lr: 7.5348e-04 lr: 7.5348e-04 eta: 0:16:52 time: 0.2542 data_time: 0.0268 memory: 1905 loss: 3.8626 loss_cls: 0.5554 loss_bbox: 2.2107 loss_obj: 0.6019 loss_l1: 0.4946 12/30 16:00:20 - mmengine - INFO - Epoch(train) [5][200/370] base_lr: 7.3560e-04 lr: 7.3560e-04 eta: 0:16:39 time: 0.2524 data_time: 0.0283 memory: 1905 loss: 3.7796 loss_cls: 0.5553 loss_bbox: 2.1931 loss_obj: 0.5653 loss_l1: 0.4658 12/30 16:00:33 - mmengine - INFO - Epoch(train) [5][250/370] base_lr: 7.1734e-04 lr: 7.1734e-04 eta: 0:16:25 time: 0.2535 data_time: 0.0265 memory: 1560 loss: 3.8750 loss_cls: 0.5639 loss_bbox: 2.2498 loss_obj: 0.5851 loss_l1: 0.4762 12/30 16:00:45 - mmengine - INFO - Epoch(train) [5][300/370] base_lr: 6.9873e-04 lr: 6.9873e-04 eta: 0:16:12 time: 0.2554 data_time: 0.0269 memory: 1905 loss: 3.7478 loss_cls: 0.5528 loss_bbox: 2.1962 loss_obj: 0.5251 loss_l1: 0.4737 12/30 16:00:58 - mmengine - INFO - Epoch(train) [5][350/370] base_lr: 6.7981e-04 lr: 6.7981e-04 eta: 0:15:59 time: 0.2518 data_time: 0.0267 memory: 1905 loss: 3.6888 loss_cls: 0.5483 loss_bbox: 2.1425 loss_obj: 0.5285 loss_l1: 0.4696 12/30 16:01:02 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:01:02 - mmengine - INFO - Saving checkpoint at 5 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:01:10 - mmengine - INFO - Epoch(val) [5][50/97] eta: 0:00:03 time: 0.0817 data_time: 0.0290 memory: 755 12/30 16:01:13 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.27s). Accumulating evaluation results... DONE (t=0.09s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.867 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.397 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.160 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.407 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.628 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.499 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.499 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.499 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.497 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.682 12/30 16:01:14 - mmengine - INFO - bbox_mAP_copypaste: 0.442 0.867 0.397 0.160 0.407 0.628 12/30 16:01:14 - mmengine - INFO - Epoch(val) [5][97/97] coco/bbox_mAP: 0.4420 coco/bbox_mAP_50: 0.8670 coco/bbox_mAP_75: 0.3970 coco/bbox_mAP_s: 0.1600 coco/bbox_mAP_m: 0.4070 coco/bbox_mAP_l: 0.6280 data_time: 0.0227 time: 0.0744 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:01:28 - mmengine - INFO - Epoch(train) [6][ 50/370] base_lr: 6.5287e-04 lr: 6.5287e-04 eta: 0:15:41 time: 0.2837 data_time: 0.0464 memory: 1905 loss: 3.7066 loss_cls: 0.5477 loss_bbox: 2.1399 loss_obj: 0.5435 loss_l1: 0.4756 12/30 16:01:41 - mmengine - INFO - Epoch(train) [6][100/370] base_lr: 6.3334e-04 lr: 6.3334e-04 eta: 0:15:28 time: 0.2555 data_time: 0.0282 memory: 1905 loss: 3.5780 loss_cls: 0.5398 loss_bbox: 2.0883 loss_obj: 0.5016 loss_l1: 0.4483 12/30 16:01:53 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:01:53 - mmengine - INFO - Epoch(train) [6][150/370] base_lr: 6.1361e-04 lr: 6.1361e-04 eta: 0:15:15 time: 0.2550 data_time: 0.0278 memory: 1905 loss: 3.6842 loss_cls: 0.5510 loss_bbox: 2.1509 loss_obj: 0.5347 loss_l1: 0.4476 12/30 16:02:06 - mmengine - INFO - Epoch(train) [6][200/370] base_lr: 5.9372e-04 lr: 5.9372e-04 eta: 0:15:01 time: 0.2499 data_time: 0.0265 memory: 1560 loss: 3.6168 loss_cls: 0.5455 loss_bbox: 2.1142 loss_obj: 0.4919 loss_l1: 0.4652 12/30 16:02:19 - mmengine - INFO - Epoch(train) [6][250/370] base_lr: 5.7372e-04 lr: 5.7372e-04 eta: 0:14:48 time: 0.2554 data_time: 0.0281 memory: 1905 loss: 3.7683 loss_cls: 0.5562 loss_bbox: 2.2057 loss_obj: 0.5366 loss_l1: 0.4698 12/30 16:02:31 - mmengine - INFO - Epoch(train) [6][300/370] base_lr: 5.5362e-04 lr: 5.5362e-04 eta: 0:14:35 time: 0.2540 data_time: 0.0270 memory: 1753 loss: 3.5672 loss_cls: 0.5421 loss_bbox: 2.0978 loss_obj: 0.4921 loss_l1: 0.4352 12/30 16:02:44 - mmengine - INFO - Epoch(train) [6][350/370] base_lr: 5.3347e-04 lr: 5.3347e-04 eta: 0:14:22 time: 0.2561 data_time: 0.0269 memory: 1408 loss: 3.5904 loss_cls: 0.5407 loss_bbox: 2.1116 loss_obj: 0.4999 loss_l1: 0.4382 12/30 16:02:49 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:02:49 - mmengine - INFO - Saving checkpoint at 6 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:02:56 - mmengine - INFO - Epoch(val) [6][50/97] eta: 0:00:03 time: 0.0789 data_time: 0.0238 memory: 1753 12/30 16:02:59 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.46s). Accumulating evaluation results... DONE (t=0.08s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.430 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.883 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.363 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.139 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.486 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.486 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.486 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.675 12/30 16:03:00 - mmengine - INFO - bbox_mAP_copypaste: 0.430 0.883 0.363 0.139 0.379 0.620 12/30 16:03:00 - mmengine - INFO - Epoch(val) [6][97/97] coco/bbox_mAP: 0.4300 coco/bbox_mAP_50: 0.8830 coco/bbox_mAP_75: 0.3630 coco/bbox_mAP_s: 0.1390 coco/bbox_mAP_m: 0.3790 coco/bbox_mAP_l: 0.6200 data_time: 0.0204 time: 0.0729 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:03:14 - mmengine - INFO - Epoch(train) [7][ 50/370] base_lr: 5.0524e-04 lr: 5.0524e-04 eta: 0:14:05 time: 0.2795 data_time: 0.0436 memory: 1560 loss: 3.7247 loss_cls: 0.5481 loss_bbox: 2.1659 loss_obj: 0.5754 loss_l1: 0.4354 12/30 16:03:26 - mmengine - INFO - Epoch(train) [7][100/370] base_lr: 4.8512e-04 lr: 4.8512e-04 eta: 0:13:51 time: 0.2524 data_time: 0.0288 memory: 1905 loss: 3.6531 loss_cls: 0.5424 loss_bbox: 2.1200 loss_obj: 0.5468 loss_l1: 0.4438 12/30 16:03:39 - mmengine - INFO - Epoch(train) [7][150/370] base_lr: 4.6507e-04 lr: 4.6507e-04 eta: 0:13:38 time: 0.2530 data_time: 0.0277 memory: 1560 loss: 3.5580 loss_cls: 0.5388 loss_bbox: 2.0886 loss_obj: 0.4959 loss_l1: 0.4347 12/30 16:03:52 - mmengine - INFO - Epoch(train) [7][200/370] base_lr: 4.4512e-04 lr: 4.4512e-04 eta: 0:13:25 time: 0.2559 data_time: 0.0266 memory: 1905 loss: 3.5768 loss_cls: 0.5412 loss_bbox: 2.0958 loss_obj: 0.5002 loss_l1: 0.4396 12/30 16:04:05 - mmengine - INFO - Epoch(train) [7][250/370] base_lr: 4.2532e-04 lr: 4.2532e-04 eta: 0:13:12 time: 0.2560 data_time: 0.0274 memory: 1408 loss: 3.4742 loss_cls: 0.5354 loss_bbox: 2.0612 loss_obj: 0.4632 loss_l1: 0.4144 12/30 16:04:18 - mmengine - INFO - Epoch(train) [7][300/370] base_lr: 4.0570e-04 lr: 4.0570e-04 eta: 0:12:59 time: 0.2566 data_time: 0.0262 memory: 1408 loss: 3.5340 loss_cls: 0.5343 loss_bbox: 2.0805 loss_obj: 0.4780 loss_l1: 0.4412 12/30 16:04:32 - mmengine - INFO - Epoch(train) [7][350/370] base_lr: 3.8629e-04 lr: 3.8629e-04 eta: 0:12:48 time: 0.2823 data_time: 0.0293 memory: 1753 loss: 3.4794 loss_cls: 0.5336 loss_bbox: 2.0361 loss_obj: 0.4798 loss_l1: 0.4299 12/30 16:04:36 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:04:36 - mmengine - INFO - Saving checkpoint at 7 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:04:44 - mmengine - INFO - Epoch(val) [7][50/97] eta: 0:00:03 time: 0.0789 data_time: 0.0238 memory: 1408 12/30 16:04:47 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.27s). Accumulating evaluation results... DONE (t=0.08s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.856 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.409 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.380 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.459 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.720 12/30 16:04:47 - mmengine - INFO - bbox_mAP_copypaste: 0.448 0.856 0.409 0.154 0.380 0.672 12/30 16:04:47 - mmengine - INFO - Epoch(val) [7][97/97] coco/bbox_mAP: 0.4480 coco/bbox_mAP_50: 0.8560 coco/bbox_mAP_75: 0.4090 coco/bbox_mAP_s: 0.1540 coco/bbox_mAP_m: 0.3800 coco/bbox_mAP_l: 0.6720 data_time: 0.0203 time: 0.0734 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:05:01 - mmengine - INFO - Epoch(train) [8][ 50/370] base_lr: 3.5955e-04 lr: 3.5955e-04 eta: 0:12:30 time: 0.2795 data_time: 0.0437 memory: 1905 loss: 3.3907 loss_cls: 0.5280 loss_bbox: 1.9989 loss_obj: 0.4477 loss_l1: 0.4161 12/30 16:05:14 - mmengine - INFO - Epoch(train) [8][100/370] base_lr: 3.4081e-04 lr: 3.4081e-04 eta: 0:12:17 time: 0.2549 data_time: 0.0266 memory: 1753 loss: 3.3950 loss_cls: 0.5277 loss_bbox: 2.0174 loss_obj: 0.4470 loss_l1: 0.4029 12/30 16:05:27 - mmengine - INFO - Epoch(train) [8][150/370] base_lr: 3.2239e-04 lr: 3.2239e-04 eta: 0:12:04 time: 0.2526 data_time: 0.0277 memory: 1408 loss: 3.6617 loss_cls: 0.5490 loss_bbox: 2.1762 loss_obj: 0.5097 loss_l1: 0.4269 12/30 16:05:39 - mmengine - INFO - Epoch(train) [8][200/370] base_lr: 3.0434e-04 lr: 3.0434e-04 eta: 0:11:51 time: 0.2548 data_time: 0.0282 memory: 1560 loss: 3.4660 loss_cls: 0.5289 loss_bbox: 2.0490 loss_obj: 0.4879 loss_l1: 0.4003 12/30 16:05:52 - mmengine - INFO - Epoch(train) [8][250/370] base_lr: 2.8669e-04 lr: 2.8669e-04 eta: 0:11:38 time: 0.2548 data_time: 0.0263 memory: 1120 loss: 3.5527 loss_cls: 0.5375 loss_bbox: 2.1057 loss_obj: 0.5075 loss_l1: 0.4020 12/30 16:06:05 - mmengine - INFO - Epoch(train) [8][300/370] base_lr: 2.6946e-04 lr: 2.6946e-04 eta: 0:11:25 time: 0.2554 data_time: 0.0283 memory: 1905 loss: 3.2652 loss_cls: 0.5064 loss_bbox: 1.8796 loss_obj: 0.4705 loss_l1: 0.4086 12/30 16:06:18 - mmengine - INFO - Epoch(train) [8][350/370] base_lr: 2.5270e-04 lr: 2.5270e-04 eta: 0:11:12 time: 0.2556 data_time: 0.0265 memory: 1905 loss: 3.5148 loss_cls: 0.5380 loss_bbox: 2.0908 loss_obj: 0.4703 loss_l1: 0.4157 12/30 16:06:22 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:06:22 - mmengine - INFO - Saving checkpoint at 8 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:06:30 - mmengine - INFO - Epoch(val) [8][50/97] eta: 0:00:03 time: 0.0788 data_time: 0.0232 memory: 1905 12/30 16:06:33 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.26s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.441 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.851 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.403 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.130 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.374 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.671 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.462 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.715 12/30 16:06:33 - mmengine - INFO - bbox_mAP_copypaste: 0.441 0.851 0.403 0.130 0.374 0.671 12/30 16:06:33 - mmengine - INFO - Epoch(val) [8][97/97] coco/bbox_mAP: 0.4410 coco/bbox_mAP_50: 0.8510 coco/bbox_mAP_75: 0.4030 coco/bbox_mAP_s: 0.1300 coco/bbox_mAP_m: 0.3740 coco/bbox_mAP_l: 0.6710 data_time: 0.0201 time: 0.0729 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:06:45 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:06:47 - mmengine - INFO - Epoch(train) [9][ 50/370] base_lr: 2.3006e-04 lr: 2.3006e-04 eta: 0:10:54 time: 0.2811 data_time: 0.0476 memory: 1905 loss: 3.3238 loss_cls: 0.5194 loss_bbox: 1.9693 loss_obj: 0.4267 loss_l1: 0.4084 12/30 16:07:00 - mmengine - INFO - Epoch(train) [9][100/370] base_lr: 2.1452e-04 lr: 2.1452e-04 eta: 0:10:41 time: 0.2505 data_time: 0.0262 memory: 1905 loss: 3.3592 loss_cls: 0.5233 loss_bbox: 1.9871 loss_obj: 0.4337 loss_l1: 0.4151 12/30 16:07:13 - mmengine - INFO - Epoch(train) [9][150/370] base_lr: 1.9955e-04 lr: 1.9955e-04 eta: 0:10:28 time: 0.2546 data_time: 0.0286 memory: 1753 loss: 3.3209 loss_cls: 0.5185 loss_bbox: 1.9691 loss_obj: 0.4302 loss_l1: 0.4030 12/30 16:07:25 - mmengine - INFO - Epoch(train) [9][200/370] base_lr: 1.8516e-04 lr: 1.8516e-04 eta: 0:10:15 time: 0.2535 data_time: 0.0294 memory: 1905 loss: 3.2102 loss_cls: 0.5096 loss_bbox: 1.9029 loss_obj: 0.4094 loss_l1: 0.3883 12/30 16:07:38 - mmengine - INFO - Epoch(train) [9][250/370] base_lr: 1.7138e-04 lr: 1.7138e-04 eta: 0:10:02 time: 0.2525 data_time: 0.0282 memory: 1269 loss: 3.4152 loss_cls: 0.5262 loss_bbox: 2.0277 loss_obj: 0.4590 loss_l1: 0.4024 12/30 16:07:51 - mmengine - INFO - Epoch(train) [9][300/370] base_lr: 1.5824e-04 lr: 1.5824e-04 eta: 0:09:49 time: 0.2544 data_time: 0.0281 memory: 1269 loss: 3.3087 loss_cls: 0.5235 loss_bbox: 1.9910 loss_obj: 0.4158 loss_l1: 0.3783 12/30 16:08:03 - mmengine - INFO - Epoch(train) [9][350/370] base_lr: 1.4576e-04 lr: 1.4576e-04 eta: 0:09:36 time: 0.2536 data_time: 0.0278 memory: 1269 loss: 3.2522 loss_cls: 0.5148 loss_bbox: 1.9393 loss_obj: 0.4236 loss_l1: 0.3745 12/30 16:08:08 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:08:08 - mmengine - INFO - Saving checkpoint at 9 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:08:15 - mmengine - INFO - Epoch(val) [9][50/97] eta: 0:00:03 time: 0.0788 data_time: 0.0231 memory: 1753 12/30 16:08:18 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.25s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.931 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.455 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.421 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.683 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.491 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.729 12/30 16:08:19 - mmengine - INFO - bbox_mAP_copypaste: 0.496 0.931 0.455 0.253 0.421 0.683 12/30 16:08:19 - mmengine - INFO - Epoch(val) [9][97/97] coco/bbox_mAP: 0.4960 coco/bbox_mAP_50: 0.9310 coco/bbox_mAP_75: 0.4550 coco/bbox_mAP_s: 0.2530 coco/bbox_mAP_m: 0.4210 coco/bbox_mAP_l: 0.6830 data_time: 0.0203 time: 0.0726 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:08:33 - mmengine - INFO - Epoch(train) [10][ 50/370] base_lr: 1.2944e-04 lr: 1.2944e-04 eta: 0:09:18 time: 0.2821 data_time: 0.0470 memory: 1753 loss: 3.2433 loss_cls: 0.5138 loss_bbox: 1.9269 loss_obj: 0.4173 loss_l1: 0.3853 12/30 16:08:46 - mmengine - INFO - Epoch(train) [10][100/370] base_lr: 1.1863e-04 lr: 1.1863e-04 eta: 0:09:05 time: 0.2550 data_time: 0.0271 memory: 1905 loss: 3.2061 loss_cls: 0.5109 loss_bbox: 1.9114 loss_obj: 0.3965 loss_l1: 0.3873 12/30 16:08:58 - mmengine - INFO - Epoch(train) [10][150/370] base_lr: 1.0856e-04 lr: 1.0856e-04 eta: 0:08:52 time: 0.2536 data_time: 0.0275 memory: 1753 loss: 3.2830 loss_cls: 0.5135 loss_bbox: 1.9357 loss_obj: 0.4161 loss_l1: 0.4177 12/30 16:09:11 - mmengine - INFO - Epoch(train) [10][200/370] base_lr: 9.9238e-05 lr: 9.9238e-05 eta: 0:08:39 time: 0.2560 data_time: 0.0267 memory: 1269 loss: 3.2819 loss_cls: 0.5218 loss_bbox: 1.9660 loss_obj: 0.4122 loss_l1: 0.3820 12/30 16:09:24 - mmengine - INFO - Epoch(train) [10][250/370] base_lr: 9.0684e-05 lr: 9.0684e-05 eta: 0:08:26 time: 0.2527 data_time: 0.0263 memory: 1560 loss: 3.2177 loss_cls: 0.5120 loss_bbox: 1.9286 loss_obj: 0.3857 loss_l1: 0.3914 12/30 16:09:36 - mmengine - INFO - Epoch(train) [10][300/370] base_lr: 8.2912e-05 lr: 8.2912e-05 eta: 0:08:13 time: 0.2510 data_time: 0.0261 memory: 1560 loss: 3.2923 loss_cls: 0.5153 loss_bbox: 1.9532 loss_obj: 0.4305 loss_l1: 0.3933 12/30 16:09:49 - mmengine - INFO - Epoch(train) [10][350/370] base_lr: 7.5937e-05 lr: 7.5937e-05 eta: 0:08:00 time: 0.2557 data_time: 0.0297 memory: 1753 loss: 3.1026 loss_cls: 0.5018 loss_bbox: 1.8645 loss_obj: 0.3732 loss_l1: 0.3631 12/30 16:09:53 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:09:53 - mmengine - INFO - Saving checkpoint at 10 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:10:01 - mmengine - INFO - Epoch(val) [10][50/97] eta: 0:00:03 time: 0.0790 data_time: 0.0240 memory: 1905 12/30 16:10:04 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.24s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.445 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.426 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.687 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.534 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.534 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.534 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.507 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.731 12/30 16:10:05 - mmengine - INFO - bbox_mAP_copypaste: 0.491 0.928 0.445 0.221 0.426 0.687 12/30 16:10:05 - mmengine - INFO - Epoch(val) [10][97/97] coco/bbox_mAP: 0.4910 coco/bbox_mAP_50: 0.9280 coco/bbox_mAP_75: 0.4450 coco/bbox_mAP_s: 0.2210 coco/bbox_mAP_m: 0.4260 coco/bbox_mAP_l: 0.6870 data_time: 0.0202 time: 0.0731 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:10:19 - mmengine - INFO - Epoch(train) [11][ 50/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:07:43 time: 0.2782 data_time: 0.0449 memory: 1905 loss: 3.1972 loss_cls: 0.5113 loss_bbox: 1.9066 loss_obj: 0.3860 loss_l1: 0.3933 12/30 16:10:31 - mmengine - INFO - Epoch(train) [11][100/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:07:30 time: 0.2527 data_time: 0.0249 memory: 1753 loss: 3.2171 loss_cls: 0.5073 loss_bbox: 1.9033 loss_obj: 0.4139 loss_l1: 0.3927 12/30 16:10:44 - mmengine - INFO - Epoch(train) [11][150/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:07:17 time: 0.2541 data_time: 0.0252 memory: 1753 loss: 3.2043 loss_cls: 0.5167 loss_bbox: 1.9281 loss_obj: 0.3758 loss_l1: 0.3837 12/30 16:10:57 - mmengine - INFO - Epoch(train) [11][200/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:07:04 time: 0.2521 data_time: 0.0238 memory: 1753 loss: 3.1141 loss_cls: 0.5002 loss_bbox: 1.8574 loss_obj: 0.3864 loss_l1: 0.3702 12/30 16:11:09 - mmengine - INFO - Epoch(train) [11][250/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:06:51 time: 0.2518 data_time: 0.0261 memory: 1560 loss: 3.1957 loss_cls: 0.5070 loss_bbox: 1.9021 loss_obj: 0.4091 loss_l1: 0.3775 12/30 16:11:22 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:11:22 - mmengine - INFO - Epoch(train) [11][300/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:06:38 time: 0.2524 data_time: 0.0251 memory: 1905 loss: 3.2141 loss_cls: 0.5106 loss_bbox: 1.9108 loss_obj: 0.3944 loss_l1: 0.3984 12/30 16:11:35 - mmengine - INFO - Epoch(train) [11][350/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:06:25 time: 0.2539 data_time: 0.0250 memory: 1905 loss: 3.0642 loss_cls: 0.4944 loss_bbox: 1.8226 loss_obj: 0.3814 loss_l1: 0.3658 12/30 16:11:39 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:11:39 - mmengine - INFO - Saving checkpoint at 11 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:11:47 - mmengine - INFO - Epoch(val) [11][50/97] eta: 0:00:03 time: 0.0802 data_time: 0.0239 memory: 1408 12/30 16:11:50 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.24s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.493 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.919 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.458 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.426 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.696 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.537 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.537 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.537 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.500 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.742 12/30 16:11:50 - mmengine - INFO - bbox_mAP_copypaste: 0.493 0.919 0.458 0.219 0.426 0.696 12/30 16:11:50 - mmengine - INFO - Epoch(val) [11][97/97] coco/bbox_mAP: 0.4930 coco/bbox_mAP_50: 0.9190 coco/bbox_mAP_75: 0.4580 coco/bbox_mAP_s: 0.2190 coco/bbox_mAP_m: 0.4260 coco/bbox_mAP_l: 0.6960 data_time: 0.0206 time: 0.0733 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:12:04 - mmengine - INFO - Epoch(train) [12][ 50/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:06:07 time: 0.2791 data_time: 0.0443 memory: 1905 loss: 3.2347 loss_cls: 0.5112 loss_bbox: 1.9280 loss_obj: 0.4124 loss_l1: 0.3831 12/30 16:12:17 - mmengine - INFO - Epoch(train) [12][100/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:05:54 time: 0.2549 data_time: 0.0272 memory: 1905 loss: 3.1935 loss_cls: 0.5075 loss_bbox: 1.8906 loss_obj: 0.3895 loss_l1: 0.4059 12/30 16:12:29 - mmengine - INFO - Epoch(train) [12][150/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:05:41 time: 0.2523 data_time: 0.0257 memory: 1120 loss: 3.1417 loss_cls: 0.5035 loss_bbox: 1.8888 loss_obj: 0.3866 loss_l1: 0.3628 12/30 16:12:42 - mmengine - INFO - Epoch(train) [12][200/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:05:28 time: 0.2537 data_time: 0.0249 memory: 1905 loss: 3.1326 loss_cls: 0.5017 loss_bbox: 1.8545 loss_obj: 0.3964 loss_l1: 0.3800 12/30 16:12:55 - mmengine - INFO - Epoch(train) [12][250/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:05:15 time: 0.2561 data_time: 0.0244 memory: 1905 loss: 3.2112 loss_cls: 0.5096 loss_bbox: 1.9090 loss_obj: 0.4106 loss_l1: 0.3819 12/30 16:13:08 - mmengine - INFO - Epoch(train) [12][300/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:05:03 time: 0.2541 data_time: 0.0256 memory: 1905 loss: 3.1101 loss_cls: 0.4966 loss_bbox: 1.8388 loss_obj: 0.3987 loss_l1: 0.3760 12/30 16:13:20 - mmengine - INFO - Epoch(train) [12][350/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:04:50 time: 0.2529 data_time: 0.0265 memory: 1269 loss: 3.1166 loss_cls: 0.5011 loss_bbox: 1.8735 loss_obj: 0.3872 loss_l1: 0.3548 12/30 16:13:25 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:13:25 - mmengine - INFO - Saving checkpoint at 12 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:13:32 - mmengine - INFO - Epoch(val) [12][50/97] eta: 0:00:03 time: 0.0789 data_time: 0.0235 memory: 1120 12/30 16:13:35 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.25s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.510 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.931 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.472 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.238 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.451 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.702 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.746 12/30 16:13:36 - mmengine - INFO - bbox_mAP_copypaste: 0.510 0.931 0.472 0.238 0.451 0.702 12/30 16:13:36 - mmengine - INFO - Epoch(val) [12][97/97] coco/bbox_mAP: 0.5100 coco/bbox_mAP_50: 0.9310 coco/bbox_mAP_75: 0.4720 coco/bbox_mAP_s: 0.2380 coco/bbox_mAP_m: 0.4510 coco/bbox_mAP_l: 0.7020 data_time: 0.0203 time: 0.0727 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:13:50 - mmengine - INFO - Epoch(train) [13][ 50/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:04:32 time: 0.2815 data_time: 0.0474 memory: 1905 loss: 3.1867 loss_cls: 0.5055 loss_bbox: 1.8802 loss_obj: 0.4063 loss_l1: 0.3947 12/30 16:14:03 - mmengine - INFO - Epoch(train) [13][100/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:04:19 time: 0.2535 data_time: 0.0251 memory: 1905 loss: 3.1622 loss_cls: 0.5052 loss_bbox: 1.8914 loss_obj: 0.3727 loss_l1: 0.3928 12/30 16:14:15 - mmengine - INFO - Epoch(train) [13][150/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:04:06 time: 0.2544 data_time: 0.0243 memory: 1269 loss: 3.1312 loss_cls: 0.5060 loss_bbox: 1.8998 loss_obj: 0.3768 loss_l1: 0.3485 12/30 16:14:29 - mmengine - INFO - Epoch(train) [13][200/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:03:53 time: 0.2770 data_time: 0.0260 memory: 1408 loss: 3.1343 loss_cls: 0.5055 loss_bbox: 1.8876 loss_obj: 0.3758 loss_l1: 0.3655 12/30 16:14:42 - mmengine - INFO - Epoch(train) [13][250/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:03:40 time: 0.2528 data_time: 0.0249 memory: 1408 loss: 3.0802 loss_cls: 0.4972 loss_bbox: 1.8308 loss_obj: 0.3949 loss_l1: 0.3573 12/30 16:14:55 - mmengine - INFO - Epoch(train) [13][300/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:03:28 time: 0.2544 data_time: 0.0261 memory: 1120 loss: 3.2652 loss_cls: 0.5179 loss_bbox: 1.9564 loss_obj: 0.4215 loss_l1: 0.3695 12/30 16:15:07 - mmengine - INFO - Epoch(train) [13][350/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:03:15 time: 0.2546 data_time: 0.0262 memory: 1753 loss: 3.1933 loss_cls: 0.5064 loss_bbox: 1.8802 loss_obj: 0.3996 loss_l1: 0.4071 12/30 16:15:11 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:15:11 - mmengine - INFO - Saving checkpoint at 13 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:15:19 - mmengine - INFO - Epoch(val) [13][50/97] eta: 0:00:03 time: 0.0789 data_time: 0.0235 memory: 1120 12/30 16:15:22 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.25s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.874 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.411 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.683 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.502 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.502 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.502 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.728 12/30 16:15:22 - mmengine - INFO - bbox_mAP_copypaste: 0.458 0.874 0.411 0.153 0.382 0.683 12/30 16:15:23 - mmengine - INFO - Epoch(val) [13][97/97] coco/bbox_mAP: 0.4580 coco/bbox_mAP_50: 0.8740 coco/bbox_mAP_75: 0.4110 coco/bbox_mAP_s: 0.1530 coco/bbox_mAP_m: 0.3820 coco/bbox_mAP_l: 0.6830 data_time: 0.0201 time: 0.0727 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:15:37 - mmengine - INFO - Epoch(train) [14][ 50/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:02:57 time: 0.2806 data_time: 0.0436 memory: 1753 loss: 3.1843 loss_cls: 0.5072 loss_bbox: 1.9083 loss_obj: 0.3919 loss_l1: 0.3770 12/30 16:15:49 - mmengine - INFO - Epoch(train) [14][100/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:02:44 time: 0.2562 data_time: 0.0248 memory: 1905 loss: 3.2161 loss_cls: 0.5132 loss_bbox: 1.9266 loss_obj: 0.3959 loss_l1: 0.3805 12/30 16:16:02 - mmengine - INFO - Epoch(train) [14][150/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:02:31 time: 0.2496 data_time: 0.0247 memory: 1905 loss: 3.1652 loss_cls: 0.5052 loss_bbox: 1.8848 loss_obj: 0.3734 loss_l1: 0.4018 12/30 16:16:12 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:16:15 - mmengine - INFO - Epoch(train) [14][200/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:02:18 time: 0.2530 data_time: 0.0249 memory: 1560 loss: 3.2158 loss_cls: 0.5149 loss_bbox: 1.9437 loss_obj: 0.3893 loss_l1: 0.3680 12/30 16:16:27 - mmengine - INFO - Epoch(train) [14][250/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:02:05 time: 0.2541 data_time: 0.0253 memory: 1560 loss: 3.0076 loss_cls: 0.4909 loss_bbox: 1.7960 loss_obj: 0.3521 loss_l1: 0.3686 12/30 16:16:40 - mmengine - INFO - Epoch(train) [14][300/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:01:52 time: 0.2554 data_time: 0.0247 memory: 1905 loss: 3.1175 loss_cls: 0.5027 loss_bbox: 1.8704 loss_obj: 0.3730 loss_l1: 0.3714 12/30 16:16:53 - mmengine - INFO - Epoch(train) [14][350/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:01:40 time: 0.2548 data_time: 0.0256 memory: 1408 loss: 3.2559 loss_cls: 0.5173 loss_bbox: 1.9586 loss_obj: 0.4129 loss_l1: 0.3671 12/30 16:16:57 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:16:57 - mmengine - INFO - Saving checkpoint at 14 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:17:05 - mmengine - INFO - Epoch(val) [14][50/97] eta: 0:00:03 time: 0.0791 data_time: 0.0234 memory: 1269 12/30 16:17:08 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.24s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.516 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.942 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.477 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.455 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.706 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.520 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.751 12/30 16:17:08 - mmengine - INFO - bbox_mAP_copypaste: 0.516 0.942 0.477 0.250 0.455 0.706 12/30 16:17:08 - mmengine - INFO - Epoch(val) [14][97/97] coco/bbox_mAP: 0.5160 coco/bbox_mAP_50: 0.9420 coco/bbox_mAP_75: 0.4770 coco/bbox_mAP_s: 0.2500 coco/bbox_mAP_m: 0.4550 coco/bbox_mAP_l: 0.7060 data_time: 0.0202 time: 0.0729 /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py:118: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:17:22 - mmengine - INFO - Epoch(train) [15][ 50/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:01:22 time: 0.2777 data_time: 0.0413 memory: 1753 loss: 3.2400 loss_cls: 0.5106 loss_bbox: 1.9270 loss_obj: 0.4074 loss_l1: 0.3951 12/30 16:17:35 - mmengine - INFO - Epoch(train) [15][100/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:01:09 time: 0.2537 data_time: 0.0249 memory: 1905 loss: 3.2051 loss_cls: 0.5113 loss_bbox: 1.9137 loss_obj: 0.4010 loss_l1: 0.3791 12/30 16:17:47 - mmengine - INFO - Epoch(train) [15][150/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:00:56 time: 0.2548 data_time: 0.0263 memory: 1905 loss: 3.0463 loss_cls: 0.4909 loss_bbox: 1.7943 loss_obj: 0.3743 loss_l1: 0.3869 12/30 16:18:00 - mmengine - INFO - Epoch(train) [15][200/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:00:43 time: 0.2533 data_time: 0.0250 memory: 1905 loss: 3.1253 loss_cls: 0.5057 loss_bbox: 1.8754 loss_obj: 0.3626 loss_l1: 0.3817 12/30 16:18:13 - mmengine - INFO - Epoch(train) [15][250/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:00:30 time: 0.2539 data_time: 0.0251 memory: 1905 loss: 3.1604 loss_cls: 0.5067 loss_bbox: 1.8949 loss_obj: 0.3933 loss_l1: 0.3656 12/30 16:18:25 - mmengine - INFO - Epoch(train) [15][300/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:00:17 time: 0.2518 data_time: 0.0253 memory: 1905 loss: 3.1130 loss_cls: 0.5011 loss_bbox: 1.8640 loss_obj: 0.3739 loss_l1: 0.3740 12/30 16:18:38 - mmengine - INFO - Epoch(train) [15][350/370] base_lr: 7.3373e-05 lr: 7.3373e-05 eta: 0:00:05 time: 0.2520 data_time: 0.0256 memory: 1753 loss: 3.2040 loss_cls: 0.5099 loss_bbox: 1.9022 loss_obj: 0.4029 loss_l1: 0.3890 12/30 16:18:42 - mmengine - INFO - Exp name: yolox_tiny_lite_20251230_155159 12/30 16:18:42 - mmengine - INFO - Saving checkpoint at 15 epochs /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): 12/30 16:18:50 - mmengine - INFO - Epoch(val) [15][50/97] eta: 0:00:03 time: 0.0783 data_time: 0.0236 memory: 755 12/30 16:18:53 - mmengine - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.24s). Accumulating evaluation results... DONE (t=0.07s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.931 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.469 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.238 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.439 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.696 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.509 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.743 12/30 16:18:54 - mmengine - INFO - bbox_mAP_copypaste: 0.503 0.931 0.469 0.238 0.439 0.696 12/30 16:18:54 - mmengine - INFO - Epoch(val) [15][97/97] coco/bbox_mAP: 0.5030 coco/bbox_mAP_50: 0.9310 coco/bbox_mAP_75: 0.4690 coco/bbox_mAP_s: 0.2380 coco/bbox_mAP_m: 0.4390 coco/bbox_mAP_l: 0.6960 data_time: 0.0203 time: 0.0726 12/30 16:18:55 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 12/30 16:18:55 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 12/30 16:18:55 - mmengine - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future. 12/30 16:18:55 - mmengine - INFO - Export PyTorch model to ONNX: /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx. /home/yash/TI/edgeai-tensorlab/edgeai-mmdetection/mmdet/models/backbones/csp_darknet.py:244: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(enabled=False): /home/yash/.pyenv/versions/py3102/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py:5715: UserWarning: Exporting aten::index operator of advanced indexing in opset 17 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results. warnings.warn( 12/30 16:18:58 - mmengine - INFO - Execute onnx optimize passes. Converted model is valid! ONNX export success, save into /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx SUCCESS: ModelMaker - Training completed.(py310)(py310) yash@train-server2:~/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210$ cat run.log
INFO:20251230-161903: starting - od-8210
INFO:20251230-161903: running - od-8210
INFO:20251230-161903: pipeline_config - {'task_type': 'detection', 'dataset_category': 'coco', 'calibration_dataset': <edgeai_benchmark.datasets.modelmaker_datasets.ModelMakerDetectionDataset object at 0x795bbe4d8b80>, 'input_dataset': <edgeai_benchmark.datasets.modelmaker_datasets.ModelMakerDetectionDataset object at 0x795bbe4dae30>, 'preprocess': <edgeai_benchmark.preprocess.PreProcessTransforms object at 0x795bbd9547f0>, 'session': <edgeai_benchmark.sessions.onnxrt_session.ONNXRTSession object at 0x795bbd954850>, 'postprocess': <edgeai_benchmark.postprocess.PostProcessTransforms object at 0x795bbd954b50>, 'metric': {'label_offset_pred': 1}, 'model_info': {'metric_reference': {'accuracy_ap[.5:.95]%': None}, 'model_shortlist': 20, 'compact_name': 'yolox-tiny-lite-mmdet-coco-416x416', 'shortlisted': True}}
INFO:20251230-161903: import - od-8210 - this may take some time...
INFO:20251230-161903: model_path - /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx
INFO:20251230-161903: model_file - /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/model/model.onnx
INFO:20251230-161903: quant_file - /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/model/model_qparams.prototxt
Downloading 1/1: /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx
Download done for /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx
Downloading 1/1: /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx
Download done for /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx
========================= [Model Compilation Started] =========================
Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning
============================== [Version Summary] ==============================
-------------------------------------------------------------------------------
| TIDL Tools Version | 11_00_08_00 |
-------------------------------------------------------------------------------
| C7x Firmware Version | 11_00_00_00 |
-------------------------------------------------------------------------------
| Runtime Version | 1.15.0 |
-------------------------------------------------------------------------------
| Model Opset Version | 17 |
-------------------------------------------------------------------------------
============================== [Parsing Started] ==============================
yolox is meta arch name
yolox
Number of OD backbone nodes = 189
------------------------- Subgraph Information Summary -------------------------
-------------------------------------------------------------------------------
| Core | No. of Nodes | Number of Subgraphs |
-------------------------------------------------------------------------------
| C7x | 271 | 1 |
| CPU | 0 | x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================
TIDL Meta pipeLine (proto) file : /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/model/model.prototxt
yolox
yolox
==================== [Optimization for subgraph_0 Started] ====================
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 712
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Invalid Layer Name 727
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 712
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Invalid Layer Name 727
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Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
----------------------------- Optimization Summary -----------------------------
-------------------------------------------------------------------------------------
| Layer | Nodes before optimization | Nodes after optimization |
-------------------------------------------------------------------------------------
| TIDL_OdOutputReformatLayer | 0 | 2 |
| TIDL_DetectionOutputLayer | 0 | 1 |
| TIDL_EltWiseLayer | 7 | 7 |
| TIDL_ConcatLayer | 16 | 16 |
| TIDL_ReLULayer | 74 | 0 |
| TIDL_ResizeLayer | 2 | 2 |
| TIDL_ConvolutionLayer | 84 | 84 |
| TIDL_PoolingLayer | 6 | 6 |
-------------------------------------------------------------------------------------
Total nodes in subgraph: 122
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
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Invalid Layer Name 1033
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=================== [Optimization for subgraph_0 Completed] ===================
The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
0.0s: VX_ZONE_INFO: Globally Enabled VX_ZONE_ERROR
0.7s: VX_ZONE_INFO: Globally Enabled VX_ZONE_WARNING
============= [Quantization & Calibration for subgraph_0 Started] =============
[TIDL Import] [QUANTIZATION] WARNING: Could not open /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/model/model_qparams.prototxt for importing mixed precision info.
This will be generated after model compilation.
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Invalid Layer Name 1033
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Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
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Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 1033
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Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 713
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Invalid Layer Name 1033
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Invalid Layer Name 1033
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Invalid Layer Name 1033
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Invalid Layer Name 713
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Invalid Layer Name 743
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 744
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Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
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Invalid Layer Name 743
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
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Invalid Layer Name 744
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Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
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Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 743
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
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Invalid Layer Name 1033
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Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
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Invalid Layer Name 713
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Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 713
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Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 728
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Invalid Layer Name 1033
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Invalid Layer Name 1033
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Invalid Layer Name 1033
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Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
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Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
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Invalid Layer Name 1033
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Invalid Layer Name 713
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Invalid Layer Name 728
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Invalid Layer Name 713
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Invalid Layer Name 728
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Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
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Invalid Layer Name 728
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Invalid Layer Name 711
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Invalid Layer Name 713
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
Invalid Layer Name 743
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Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
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Invalid Layer Name 728
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Invalid Layer Name 713
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Invalid Layer Name 728
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Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
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Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
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Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
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------------------------- Network Compiler Traces ------------------------------
Successful Memory Allocation
Successful Workload Creation
------------------------- Network Compiler Traces ------------------------------
Successful Memory Allocation
Successful Workload Creation
INFO:20251230-162303: import completed - od-8210 - 240 sec
SUCCESS:20251230-162303: benchmark results - {}
Invalid Layer Name 1033
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Invalid Layer Name 711
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Invalid Layer Name 1033
Invalid Layer Name 711
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Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Invalid Layer Name 1033
Invalid Layer Name 711
Invalid Layer Name 712
Invalid Layer Name 713
Invalid Layer Name 727
Invalid Layer Name 728
Invalid Layer Name 728
Invalid Layer Name 743
Invalid Layer Name 744
Invalid Layer Name 745
Parameters unavailable, running calibration
-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
------------------ Fixed-point Calibration Iteration [1 / 8]: ------------------
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Parameters unavailable, running calibration
Output network quant params prototxt file path: /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/artifacts/tempDir/subgraph_0_tidl_net_quant_params.prototxt
Calibrated quant parameters stored in protoTxt format
==================== [Quantization & Calibration Completed] ====================
========================== [Memory Planning Started] ==========================
========================= [Memory Planning Completed] =========================
Rerunning network compiler...
========================== [Memory Planning Started] ==========================
========================= [Memory Planning Completed] =========================
======================== Subgraph Compiled Successfully ========================
MEM: Deinit ... !!!
MEM: Alloc's: 27 alloc's of 229850089 bytes
MEM: Free's : 27 free's of 229850089 bytes
MEM: Open's : 0 allocs of 0 bytes
MEM: Deinit ... Done !!!
INFO:20251230-162304: starting - od-8210
INFO:20251230-162304: running - od-8210
INFO:20251230-162304: pipeline_config - {'task_type': 'detection', 'dataset_category': 'coco', 'calibration_dataset': <edgeai_benchmark.datasets.modelmaker_datasets.ModelMakerDetectionDataset object at 0x795bbe4d8b80>, 'input_dataset': <edgeai_benchmark.datasets.modelmaker_datasets.ModelMakerDetectionDataset object at 0x795bbe4dae30>, 'preprocess': <edgeai_benchmark.preprocess.PreProcessTransforms object at 0x795bbd9547f0>, 'session': <edgeai_benchmark.sessions.onnxrt_session.ONNXRTSession object at 0x795bbd954850>, 'postprocess': <edgeai_benchmark.postprocess.PostProcessTransforms object at 0x795bbd954b50>, 'metric': {'label_offset_pred': 1}, 'model_info': {'metric_reference': {'accuracy_ap[.5:.95]%': None}, 'model_shortlist': 20, 'compact_name': 'yolox-tiny-lite-mmdet-coco-416x416', 'shortlisted': True}}
INFO:20251230-162304: infer - od-8210 - this may take some time...
INFO:20251230-162304: model_path - /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/training/model.onnx
INFO:20251230-162304: model_file - /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/model/model.onnx
INFO:20251230-162304: quant_file - /home/yash/TI/edgeai-tensorlab/edgeai-modelmaker/data/projects/custom_dataset/run/20251230-155147/yolox_tiny_lite/compilation/work/od-8210/model/model_qparams.prototxt
infer : od-8210 ..........(truncated as symbols not allowed)..... 770/770 [52:08<00:00, 4.06s/it]
INFO:20251230-171512: infer completed - od-8210 - 3128 sec
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.26s).
Accumulating evaluation results...
DONE (t=0.07s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.872
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.358
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.315
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.447
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.459
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676
SUCCESS:20251230-171512: benchmark results - {'infer_path': 'od-8210', 'accuracy_ap[.5:.95]%': 41.262896, 'accuracy_ap50%': 87.154001, 'num_subgraphs': 1, 'perfsim_time_ms': 9.31835, 'perfsim_ddr_transfer_mb': 6.99, 'perfsim_gmacs': 3.2007}
libtidl_onnxrt_EP loaded 0x593daf705de0
Final number of subgraphs created are : 1, - Offloaded Nodes - 271, Total Nodes - 271
The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
0.0s: VX_ZONE_INFO: Globally Enabled VX_ZONE_ERROR
0.7s: VX_ZONE_INFO: Globally Enabled VX_ZONE_WARNING
MEM: Deinit ... !!!
MEM: Alloc's: 27 alloc's of 43415939 bytes
MEM: Free's : 27 free's of 43415939 bytes
MEM: Open's : 0 allocs of 0 bytes
MEM: Deinit ... Done !!!
SUCCESS: ModelMaker - Compilation completed.(py310)title: "Object Detection"
log_level: 2
inputs:
input0:
source: /dev/video-usb-cam0
format: jpeg
width: 1280
height: 720
framerate: 30
input1:
source: /opt/edgeai-test-data/videos/video0_1280_768.h264
format: h264
width: 1280
height: 768
framerate: 30
loop: True
input2:
source: /opt/edgeai-test-data/images/%04d.jpg
width: 1280
height: 720
index: 0
framerate: 1
loop: True
models:
model0:
model_path: /opt/model_zoo/TVM-OD-5120-ssdLite-mobDet-DSP-coco-320x320
viz_threshold: 0.6
model1:
model_path: /opt/model_zoo/TFL-OD-2020-ssdLite-mobDet-DSP-coco-320x320
viz_threshold: 0.6
model2:
model_path: /opt/model_zoo/ONR-OD-8220-yolox-s-lite-mmdet-coco-640x640
viz_threshold: 0.6
model3:
model_path: /opt/model_zoo/yolotiny
viz_threshold: 0.5
outputs:
output0:
sink: kmssink
width: 1920
height: 1080
overlay-perf-type: graph
output1:
sink: /opt/edgeai-test-data/output/output_video0.mkv
width: 1920
height: 1080
output2:
sink: /opt/edgeai-test-data/output/output_image_%04d.jpg
width: 1920
height: 1080
output3:
sink: remote
width: 1920
height: 1080
port: 8081
host: 127.0.0.1
encoding: jpeg
overlay-perf-type: graph
flows:
flow0: [input1,model3,output0,[320,150,1280,720]]root@j722s-evm:/opt/edgeai-gst-apps/apps_python# ./app_edgeai.py ../configs/object_detection_cust.yaml libtidl_onnxrt_EP loaded 0xfdab5f0 Final number of subgraphs created are : 1, - Offloaded Nodes - 271, Total Nodes - 271 APP: Init ... !!! 22344.352465 s: MEM: Init ... !!! 22344.352557 s: MEM: Initialized DMA HEAP (fd=5) !!! 22344.352778 s: MEM: Init ... Done !!! 22344.352812 s: IPC: Init ... !!! 22344.410140 s: IPC: Init ... Done !!! REMOTE_SERVICE: Init ... !!! REMOTE_SERVICE: Init ... Done !!! 22344.418823 s: GTC Frequency = 200 MHz APP: Init ... Done !!! 22344.419192 s: VX_ZONE_INFO: Globally Enabled VX_ZONE_ERROR 22344.419406 s: VX_ZONE_INFO: Globally Enabled VX_ZONE_WARNING 22344.419449 s: VX_ZONE_INFO: Globally Enabled VX_ZONE_INFO 22344.420632 s: VX_ZONE_INFO: [tivxPlatformCreateTargetId:169] Added target MPU-0 22344.421078 s: VX_ZONE_INFO: [tivxPlatformCreateTargetId:169] Added target MPU-1 22344.421679 s: VX_ZONE_INFO: [tivxPlatformCreateTargetId:169] Added target MPU-2 22344.422456 s: VX_ZONE_INFO: [tivxPlatformCreateTargetId:169] Added target MPU-3 22344.422742 s: VX_ZONE_INFO: [tivxInitLocal:202] Initialization Done !!! 22344.422851 s: VX_ZONE_INFO: Globally Disabled VX_ZONE_INFO 22344.443560 s: VX_ZONE_ERROR: [ownContextSendCmd:1001] Command ack message returned failure cmd_status: -1 22344.443654 s: VX_ZONE_ERROR: [ownNodeKernelInit:704] Target kernel, TIVX_CMD_NODE_CREATE failed for node node_84 22344.443685 s: VX_ZONE_ERROR: [ownNodeKernelInit:705] Please be sure the target callbacks have been registered for this core 22344.443715 s: VX_ZONE_ERROR: [ownNodeKernelInit:706] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel 22344.443750 s: VX_ZONE_ERROR: [ownGraphNodeKernelInit:793] kernel init for node 0, kernel com.ti.tidl:1:2 ... failed !!! 22344.443822 s: VX_ZONE_ERROR: [ TIDL subgraph dets ] Node kernel init failed 22344.443853 s: VX_ZONE_ERROR: [ TIDL subgraph dets ] Graph verify failed Traceback (most recent call last): File "/opt/edgeai-gst-apps/apps_python/./app_edgeai.py", line 67, in <module> main(sys.argv) File "/opt/edgeai-gst-apps/apps_python/./app_edgeai.py", line 46, in main demo = EdgeAIDemo(config) ^^^^^^^^^^^^^^^^^^ File "/opt/edgeai-gst-apps/apps_python/edge_ai_class.py", line 108, in __init__ model_obj.create_runtime() File "/usr/lib/python3.12/site-packages/edgeai_dl_inferer.py", line 315, in create_runtime self.run_time = RunTime(self.artifacts, ^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/site-packages/edgeai_dl_inferer.py", line 170, in __init__ self.interpreter = _onnxruntime.InferenceSession( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 387, in __init__ self._create_inference_session(providers, provider_options, disabled_optimizers) File "/usr/lib/python3.12/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 439, in _create_inference_session sess.initialize_session(providers, provider_options, disabled_optimizers) onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Create state function failed. Return value:-1
