wirthual/yolox-executorch

YOLOX models for executorch

YOLOX models trained on COCO object detection (118k annotated images) at resolution 640x640. It was introduced in the paper YOLOX: Exceeding YOLO Series in 2021 by Zheng Ge et al. and first released in this repository.

The models in this repo have been exported to use with executorch

Here is an example of detections created with YOLOX nano and the executorch runtime:

The models are exported from the following standard models trained on COCO:

Standard Models.

Model	size	mAPval 0.5:0.95	mAPtest 0.5:0.95	Speed V100 (ms)	Params (M)	FLOPs (G)	weights
YOLOX-s	640	40.5	40.5	9.8	9.0	26.8	github
YOLOX-m	640	46.9	47.2	12.3	25.3	73.8	github
YOLOX-l	640	49.7	50.1	14.5	54.2	155.6	github
YOLOX-x	640	51.1	51.5	17.3	99.1	281.9	github
YOLOX-Darknet53	640	47.7	48.0	11.1	63.7	185.3	github

How to use

The models have been exported using the code from this PR. It includes instructions on how to export your model so it can be executed using the executorch runtime.

Example code on how to run inference:

   import cv2
   from executorch.runtime import Runtime

    input_shape = (640,640) # (416,416) for tiny and nano
    origin_img = cv2.imread("path/to/your/image.png")
    img = cv2.resize(origin_img, input_shape)    

    runtime = Runtime.get()
    method = runtime.load_program("path/to/model/yolox_s.pte").load_method("forward")

    output = method.execute([torch.from_numpy(img).unsqueeze(0)])
    output = [o.numpy() for o in output]

    # Add postprocessing like NMS to transform to bounding boxes

How to export and use your own YOLOX model

Install the YOLOX project from here and follow these instructions:

Step1: Install executorch

run the following command to install onnxruntime:

pip install executorch

Convert Your Model to Executorch

First, you should move to by:

cd <YOLOX_HOME>

Then, you can:

1. Convert a standard YOLOX model by -n:

python3 tools/export_executorch.py --output-name yolox_s.pte -n yolox-s -c yolox_s.pth

Notes:

• -n: specify a model name. The model name must be one of the [yolox-s,m,l,x and yolox-nano, yolox-tiny, yolov3]

• -c: the model you have trained

• To customize an input shape for onnx model, modify the following code in tools/export_executorch.py:

  dummy_input = torch.randn(1, 3, exp.test_size[0], exp.test_size[1])
  

1. Convert a standard YOLOX model by -f. When using -f, the above command is equivalent to:

python3 tools/export_executorch.py --output-name yolox_s.pte -f exps/default/yolox_s.py -c yolox_s.pth

3. To convert your customized model, please use -f:

python3 tools/export_executorch.py --output-name your_yolox.pte -f exps/your_dir/your_yolox.py -c your_yolox.pth

Step3: Executorch Runtime Demo

Step1.

cd <YOLOX_HOME>/demo/executorch

Step2.

python3 executorch_inference.py -m <EXECUTORCH_MODEL_PATH> -i <IMAGE_PATH> -o <OUTPUT_DIR> -s 0.3 --input_shape 640,640

Notes:

• -m: your converted pte model
• -i: input_image
• -s: score threshold for visualization.
• --input_shape: should be consistent with the shape you used for executorch convertion.

Cite YOLOX

If you use YOLOX in your research, please cite our work by using the following BibTeX entry:

 @article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}

[ImageTag]: ./example_output.png