[0.1.1] , Aug, 2021

In short, the content of this repository is yolox with Swin-Transformer as the backbone. 简而言之，这个仓库的内容是以swin-transformer为backbone的yolox。

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance. I rewrote the version with Swin-Transformer as backbone following Swin-Transformer-Object-Detection(https://github.com/SwinTransformer/Swin-Transformer-Object-Detection).

First of all, due to limited time, I did not experiment on the COCO dataset. All results are built on my private dataset, which cannot be shared. The composition of my dataset is not complicated, with only one class of targets, ~ 1w training images and about ~ 1.5k test images.

I used the official Swin's pretrained model (https://github.com/microsoft/Swin-Transformer) and the detection version Swin's pretrained model (https://github.com/SwinTransformer/Swin-Transformer-Object-Detection) for experiments. My experimental results show that using COCO pre-training model works better than using ImageNet pre-training model. The pretrained model type can be set directly in the configuration file.

For YOLOX with Swin backbone, I set the depth and width factor of PANet neck part with fixed 1.00, for example, self.depth = 1.00 self.width = 1.00 in config file. I simply replaced the backbone part with Swin-T/S/B.

For example,

python tools/train.py -f exps/default/yolox_swinB_coco_.py -d 8 -b 64 --fp16 --cache

To use ImageNet pre-training, please download the pre-trained model from the [website](https://github.com/microsoft/Swin-Transformer) and place it in the ./pretrained directory.

To use COCO pre-training, please download the pre-trained model from the [website](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection) and place it in the ./pretrained directory.

• the curve of yolox_m with size 640

• the curve of yolox with swin-S backbone & size 320

• the curve of yolox with swin-S backbone & size 320