Add object detection training docs #1435
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| ```bash | ||
| git clone https://github.com/ultralytics/yolov5.git | ||
| git clone https://github.com/airockchip/yolov5.git airockchip-yolov5 | ||
| wget https://gist.githubusercontent.com/Alex-idk/9a512ca7bd263892ff6991a856f1a458/raw/e8c0c9d8d5a1a60a2bbe72c065e04a261300baac/onnx2rknn.py # This is the onnx to rknn convertion script |
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Script looks reasonable, seems like the same thing most online resources have been hinting at.
Do we wanna pull this into the photonvision repo proper?
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I think this script should be owned by the PV organization. Once we start supporting more platforms it can be updated to go from a single .onnx file to all of our supported platform.
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| #### Training Command | ||
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| Please research what each of these parameters do and adjust them to fit your dataset and training needs. Make sure to change the number of classes in the `models/yolov5s.yaml` file to how many classes are in your dataset, otherwise you will run into problems with class labeling. Currently as of `September 2024` only YOLOv5s models have been tested. |
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"you must know what you are doing" lolz.
Is there anything major you could recommend an initial user look at, just to get them started? Things like "Be careful touching these parameters" or "These parameters are inter-linked" or "Here's the values we started with for the note detector" or similar?
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The main parameters for training are dependent on your dataset and hardware. I can add a link to a docs page that explains what they do. But I'm also assuming that the people who are making a custom model already have an idea of what they are doing. The special thing about doing it for PV is the conversion to RKNN.
| Please research what each of these parameters do and adjust them to fit your dataset and training needs. Make sure to change the number of classes in the `models/yolov5s.yaml` file to how many classes are in your dataset, otherwise you will run into problems with class labeling. Currently as of `September 2024` only YOLOv5s models have been tested. | ||
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| ```bash | ||
| python train.py --img 640 --batch 16 --epochs 10 --data path/to/dataset/data.yaml --cfg 'models/yolov5s.yaml' --weights '' --cache |
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Might still need a concrete example of what data.yaml is
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It might just be best to point people towards some other resources for preparing yolov5 datasets.
| #### Export Command | ||
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| ```bash | ||
| cd /path/to/airockchip-yolov5 && python export.py --weights '/path/to/best.pt' --rknpu --include 'onnx' |
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If the instructions above were used, the path should just be airockchip-yolov5 , right?
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Rather than including this as a separate step could it be merged into the current conversion command?
My thought is people who want to share models (say the PV devs, or community members) could benefit from a single script that goes from the weights to all of our supported platforms.
| python onnx2rknn.py /path/to/best.onnx /path/to/export/best.rknn /path/to/imagePaths.txt | ||
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| If you have any questions about this process feel free to mention `alex_idk` in the PhotonVision Discord server. |
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A wise man once told me to never put my name on anything :)
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I'm going to try following these instructions and see if I can get it to work 👍 |
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I will boot up a vm later and try this process from scratch and look for anything I missed. |
| Please research what each of these parameters do and adjust them to fit your dataset and training needs. Make sure to change the number of classes in the `models/yolov5s.yaml` file to how many classes are in your dataset, otherwise you will run into problems with class labeling. Currently as of `September 2024` only YOLOv5s models have been tested. | ||
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| ```bash | ||
| python train.py --img 640 --batch 16 --epochs 10 --data path/to/dataset/data.yaml --cfg 'models/yolov5s.yaml' --weights '' --cache |
There was a problem hiding this comment.
It might just be best to point people towards some other resources for preparing yolov5 datasets.
| #### Export Command | ||
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| ```bash | ||
| cd /path/to/airockchip-yolov5 && python export.py --weights '/path/to/best.pt' --rknpu --include 'onnx' |
There was a problem hiding this comment.
Rather than including this as a separate step could it be merged into the current conversion command?
My thought is people who want to share models (say the PV devs, or community members) could benefit from a single script that goes from the weights to all of our supported platforms.
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| #### Conversion Command | ||
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| Run the script, passing in the ONNX model and a text file containing paths to images from your dataset: |
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and a text file containing paths to images from your dataset:
So we've got to create this file ourselves?
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Yeah, I can probably make a revision of the conversion script to just take a path and make the file itself.
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find /path -type f -name "*.jpg" > images.txt
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I was able to follow the instructions and got the model running on an Orange PI. I'll verify tonight if the model is working. As for the state of this PR - I think this is a huge help for the PV developers. I'm no longer concerned about the bus factor regarding training object detection models. However, I don't think these instructions would be enough for the average team. I wonder if we can just change the name of this content from "training a custom model" to "converting trained model to RKNN" for now. I think a proper "training custom model" section would require teaching how to set up an environment, installing all of the dependencies, how to label data, export to YOLO formats, training parameters, and only then conversion and uploading. |
Adds documentation for training a custom model.