YOLOX-ti-lite Object Detection Models

This repository is based on Megvii/YOLOX. As per the Official documentation, YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. It has the following major differences rom YOLOv3:

• Darknet-csp backbone instead of vanilla Darknet. Reduces complexity by 30%.
• PANet feature extractor instead of FPN.
• YOLOX detection head and label assignement is based on FCOS and OTA.
  • It uses decoupled head. There are three subheads for each detection head. They are {class score head, box head, object score head}
  • Center sampling is used that assigns the center 3x3 areas as positive.
  • Label assignment is based on a simplified version of OTA, called SimOTA.
• Anchor free object detection reduces the complexity of the Detection layer.
  • There are 3x less detection candidates compared to YOLOv3 and YOLOv5.
  • Box-decoding is much simpler than other YOLO variants as well.
• Several new augmentation techniques as in YOLOv5. E.g. Mosaic augmentation.
• All models are trained from scratch. There is no need for imagenet pretraining because of strong augmentation.
• YOLOX-Nano-ti-lite uses regular convolution instead of depth-wise convolution.

For more details, please refer to Megvii's report on Arxiv.

Official Models from Megvii.

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

Light Models.

Model	size	mAPval 0.5:0.95	Params (M)	FLOPs (G)	weights
YOLOX-Nano	416	25.8	0.91	1.08	github
YOLOX-Tiny	416	32.8	5.06	6.45	github

YOLOX-ti-lite model definition

• YOLOX-ti-lite is a version of YOLOX from TI for efficient edge deployment. This naming convention is chosen to avoid possible conflict with future release of YOLOX-lite models from Megvii.

• Here is a brief description of changes that were made to get YOLOX-ti-lite from YOLOX:

  • YOLOX has a Focus layer as the very first layer of the network. This replaces the first few heavy convolution layers that are present in YOLOv3. It reduces the complexity of the n/w by 7% and training time by 15%. However, the slice operations in Focus layer are not embedded friendly and hence we replace it with a light-weight convolution layer. Here is a pictorial description of the changes from YOLOv3 to YOLOX to YOLOX-ti-lite:

  

  • SiLU activation is not well-supported in embedded devices. it's not quantization friendly as well because of it's unbounded nature. This was observed for hSwish activation function while quantizing efficientnet. Hence, SiLU activation is replaced with ReLU.
  • SPP module with maxpool(k=13, s=1), maxpool(k=9,s=1) and maxpool(k=5,s=1) are replaced with various combinations of maxpool(k=3,s=1).Intention is to keep the receptive field and functionality same. This change will cause no difference to the model in floating-point.
    • maxpool(k=5, s=1) -> replaced with two maxpool(k=3,s=1)
    • maxpool(k=9, s=1) -> replaced with four maxpool(k=3,s=1)
    • maxpool(k=13, s=1)-> replaced with six maxpool(k=3,s=1) as shown below:

    

Models trained by TI

Model	size	mAPval 0.5:0.95	mAPval 0.5:0.95	Params (M)	FLOPs (G)	weights
YOLOX-s-ti-lite	640	39.1	57.9	9.0	26.9
YOLOX-m-ti-lite	640	45.5	64.2	25.3	73.8
YOLOX-Nano-ti-lite	416	25.8	41.6	1.87	2.06
YOLOX-Tiny-ti-lite	416	32.0	49.5	5.06	6.48

Training and Testing

Reproduce our results on COCO

python -m yolox.tools.train -n yolox-s-ti-lite -d 8 -b 64 --fp16 -o [--cache]
                               yolox-m-ti-lite
                               yolox-tiny-ti-lite
                               yolox-nano-ti-lite

• -d: number of gpu devices
• -b: total batch size, the recommended number for -b is num-gpu * 8
• --fp16: mixed precision training
• --cache: caching imgs into RAM to accelarate training, which need large system RAM.

When using -f, the above commands are equivalent to:

python -m yolox.tools.train -f exps/default/yolox_s_ti_lite.py -d 8 -b 64 --fp16 -o [--cache]
                               exps/default/yolox_m_ti_lite.py
                               exps/default/yolox_tiny_ti_lite.py
                               exps/default/yolox_nano_ti_lite.py

Evaluation

python -m yolox.tools.eval -n  yolox-s-ti-lite -c yolox_s.pth -b 64 -d 8 --conf 0.001 [--fp16] [--fuse]
                               yolox-m-ti-lite
                               yolox-tiny-ti-lite
                               yolox-nano-ti-lite

• --fuse: fuse conv and bn
• -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used.
• -b: total batch size across on all GPUs

Deployment

ONNX export and an ONNXRuntime

ONNX export including detection:

Run the following commands to export the entire models including the detection part. Use the flag "export-det" to add the extra post-processing.

1. Convert a standard YOLOX model by -n:

python3 tools/export_onnx.py --output-name yolox_s.onnx -n yolox-s -c yolox_s.pth --export-det

Notes:

• -n: specify a model name. The model name must be one of the [yolox-s,m 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.py:

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

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

python3 tools/export_onnx.py --output-name yolox_s.onnx -f exps/default/yolox_s.py -c yolox_s.pth --export-det

• Apart from exporting the complete ONNX model, above script will generate a prototxt file that contains information of the detection layer. This prototxt file is required to deploy the moodel on TI SoC.

ONNXRuntime Demo

Step1.

cd <YOLOX_HOME>/demo/ONNXRuntime

• With models that are exported end-to-end, inference becomes much simpler. The model takes an image as i/p and gives out the final detection. There is no need for any further post-processing. WHile inferring with a complete model, use the export-det flag as shown below

Step2.

python3 onnx_inference.py -m <ONNX_MODEL_PATH> -i <IMAGE_PATH> -o <OUTPUT_DIR> -s 0.3 --input_shape 640,640 --export-det

Notes:

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

Pretrained Models

Pretrained models will be added under pretrained_models

References

[1] Official YOLOX repository
[2] Official YOLOV5 repository
[3] Focus layer
[4] CrossStagePartial Network
[5] Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh. CSPNet: A new backbone that can enhance learning capability of cnn. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR Workshop),2020.
[6]Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. Path aggregation network for instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8759–8768, 2018
[7] Efficientnet-lite quantization
[8] FCOS: Fully Convolutional One-Stage Object Detection
[9] OTA: Optimal Transport Assignment for Object Detection