This project is my library of One-stage Object Detection. My code is clean and concise, without too deep encapsulation, so it is easy to understand the function of each module.

For example,

• If you want to know how to pull data from COCO, you just open dataset/coco.py.
• If you want to know how to build RetinaNet, you just open models/detector/retinanet/ and config/retinanet_config.py.
• If you want to know how to build FPN, you just open models/neck/fpn.py/.
• If you want to know the whole pipeline of training, you just open train.py.
• If you want to know the whole pipeline of evaluation, you just open eval.py and evaluator/coco_evaluator.py.
• If you want to know how to visualize the detection results on detection benchmark like COCO or VOC, you just open test.py.
• If you want to know how to run a demo with images or videos on your own device, you just open demo.py.
• If you want to know how to run a demo with your own camero, you just also open demo.py.

So, it is very friendly, right?

I am sure you will soon become familiar with this benchmark and add your own modules to it.

However, limited by my computing resources, I cannot use larger backbone networks like ResNet-101 and ResNeXt-101 to train more powerful detectors. If you have sufficient computing resources and are already using these larger backbone networks to train the detectors in this benchmark, I look forward to your open source weight files to complement this project. Thanks a lot.

• YOLOF
• RetinaNet
• FCOS

• We recommend you to use Anaconda to create a conda environment:

conda create -n detection python=3.6

• Then, activate the environment:

conda activate detection

• Requirements:

pip install -r requirements.txt 

We suggest that PyTorch should be higher than 1.9.0 and Torchvision should be higher than 0.10.3. At least, please make sure your torch is version 1.x.

In my RetinaNet:

• For regression head, GIoU Loss is deployed rather than SmoothL1Loss

In my FCOS:

• For regression head, GIoU loss is deployed rather than IoU loss
• For real-time FCOS, the PaFPN is deployed for fpn

sh train.sh

You can change the configurations of train.sh, according to your own situation.

sh train_ddp.sh

You can change the configurations of train_ddp.sh, according to your own situation.

python test.py -d coco \
               --cuda \
               -v yolof50 \
               --weight path/to/weight \
               --root path/to/dataset/ \
               --show

I have provide some images in data/demo/images/, so you can run following command to run a demo:

python demo.py --mode image \
               --path_to_img data/demo/images/ \
               -v yolof50 \
               --cuda \
               --weight path/to/weight \
               --show

If you want run a demo of streaming video detection, you need to set --mode to video, and give the path to video --path_to_vid。

python demo.py --mode video \
               --path_to_img data/demo/videos/your_video \
               -v yolof50 \
               --cuda \
               --weight path/to/weight \
               --show

If you want run video detection with your camera, you need to set --mode to camera。

python demo.py --mode camera \
               -v yolof50 \
               --cuda \
               --weight path/to/weight \
               --show