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AM62A1-Q1: EdgeAI - modelmaker

Part Number: AM62A1-Q1

Tool/software:

Hi Champs,

Customer has experimented with this tool https://github.com/TexasInstruments/edgeai-tensorlab/tree/main/edgeai-modelmaker  and found the following problem:

Quote"

I wanted to use the training data from this site https://universe.roboflow.com/search?q=traffic%2520light and it works because the descriptions are an acceptable COCO format.

Unfortunately, after placing the model in AM62A in the model_zoo directory and running with the test video, I did not get any effect. The model does not recognize anything.

 

Therefore, I performed another test:

  1. Training the model with sample data from TI. I used input_data_path: 'http://software-dl.ti.com/jacinto7/esd/modelzoo/08_06_00_01/datasets/tiscapes2017_driving.zip'

and config_detection.yaml script.

  1. I moved the trained model to the model_zoo directory as the 20250428-145500_yolox_nano_lite_onnxrt_AM62A
  2. I ran the test with the test video and recording the result as output. My script attached as test1.yaml
  3. The model does not recognize anything.  See tiscapes2017_driving.mp4
  4. I attach logs from training the model logi.txt and run.log

 

My question is if I have forgotten something and what the problem could be?

 

BTW

As far as I can see, the model is trained to the universal onnx format. Is it possible to simulate this model outside the system i.e.  AM62A ?

https://dev.ti.com/modelcomposer it is still only available in the older version 9.1

Is there a converter from the COCO format to a format that is supported by https://dev.ti.com/edgeaistudio/

"

(py310) ciupakt@RZE-CIUPAKTLX-N:~/Projects/edgeai-tensorlab/edgeai-modelmaker$ ./run_modelmaker.sh AM62A config_detection.yaml 
Number of AVX cores detected in PC: 12
AVX compilation speedup in PC     : 1
Target device                     : AM62A
PYTHONPATH                        : .:
TIDL_TOOLS_PATH                   : ../edgeai-benchmark/tools/tidl_tools_package/AM62A/tidl_tools
LD_LIBRARY_PATH                   : ../edgeai-benchmark/tools/tidl_tools_package/AM62A/tidl_tools:
argv: ['./scripts/run_modelmaker.py', 'config_detection.yaml', '--target_device', 'AM62A']
---------------------------------------------------------------------
INFO: ModelMaker - task_type:detection model_name:yolox_nano_lite dataset_name:tiscapes2017_driving run_name:20250428-145500/yolox_nano_lite
- Model: yolox_nano_lite
- TargetDevices & Estimated Inference Times (ms): {'TDA4VM': 3.74, 'AM62A': 8.87, 'AM67A': '8.87 (with 1/2 device capability)', 'AM68A': 3.73, 'AM69A': '3.64 (with 1/4th device capability)', 'AM62': 516.15}
- This model can be compiled for the above device(s).
---------------------------------------------------------------------
INFO: ModelMaker - dataset split sizes {'train': 393, 'val': 107}
INFO: ModelMaker - max_num_files is set to: 10000
INFO: ModelMaker - dataset split sizes are limited to: {'train': 393, 'val': 107}
INFO: ModelMaker - dataset loading OK
loading annotations into memory...
Done (t=0.06s)
creating index...
index created!
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
INFO: ModelMaker - run params is at: /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/run.yaml
INFO: ModelMaker - running training - for detailed info see the log file: /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/training/run.log
TASKS TOTAL=1, NUM_RUNNING=0: 100%|██████████████████████████| 1/1 [03:54<00:00, 234.09s/it, postfix={'RUNNING': [], 'COMPLETED': ['yolox_nano_lite']}]
Trained model is at: /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/training

SUCCESS: ModelMaker - Training completed.
INFO: ModelMaker - running compilation - for detailed info see the log file: /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/compilation/AM62A/work/od-8200/run.log

INFO:20250428-145856: number of configs - 1
TASKS TOTAL=1, NUM_RUNNING=0: 100%|█████████████████████████████████████| 1/1 [03:56<00:00,  2.01s/it, postfix={'RUNNING': [], 'COMPLETED': ['od-8200']}]
SUCCESS: Benchmark - completed: 1/1
TASKS TOTAL=1, NUM_RUNNING=0: 100%|████████████████████████████████████| 1/1 [03:57<00:00, 237.05s/it, postfix={'RUNNING': [], 'COMPLETED': ['od-8200']}]
INFO: packaging artifacts to /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/compilation/AM62A/pkg please wait...
SUCCESS:20250428-150254: finished packaging - /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/compilation/AM62A/work/od-8200
Compiled model is at: /home/ciupakt/Projects/edgeai-tensorlab/edgeai-modelmaker/data/projects/tiscapes2017_driving/run/20250428-145500/yolox_nano_lite/compilation/AM62A/pkg/20250428-145500_yolox_nano_lite_onnxrt_AM62A.tar.gz

SUCCESS: ModelMaker - Compilation completed.





2185.run.loghttps://e2e.ti.com/cfs-file/__key/communityserver-discussions-components-files/791/test1.yaml

  • Hi Tamas, 

    It our pleasure to help with this question. Would you please consider the following?

    • What edgeai-modelmaker are they using?
    • After they trained the model, how did the model perform on the validation/test pictures? Please check the outputs folder with annotated images located at edgeai-modelmaker/data/projects/<dataset-name>/run/date-time/yolox_nano_lite/compilation/AM62A/work/od-8200/outputs. How well did the model perform on these pictures?
    • Could you please share the config file you used for the training?

    As far as I can see, the model is trained to the universal onnx format. Is it possible to simulate this model outside the system i.e.  AM62A ?

    I am not sure I understand this question very well. The onnx file of the model can be executed using the onnxruntime using on any system. However, the artifacts can be exeuted only on the device or on host emulation using edgeai-tidl-tools. For host emulation see https://github.com/TexasInstruments/edgeai-tidl-tools/tree/master/examples/osrt_python#model-inference-on-pc-optional

    Is there a converter from the COCO format to a format that is supported by https://dev.ti.com/edgeaistudio/

    The model composer support the coco format by default similar to the modelmaker. 

    Best regards,

    Qutaiba