This is a PyTorch version of YOLOF.
- We recommend you to use Anaconda to create a conda environment:
conda create -n yolof python=3.6- Then, activate the environment:
conda activate yolof- 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.
My environment:
- Torch = 1.9.1
- Torchvision = 0.10.1
- AP results of YOLOF
| Model | scale | FPS | AP | AP50 | Weight |
|---|---|---|---|---|---|
| YOLOF-R18-C5_1x | 800,1333 | 100 | 32.6 | 51.3 | github |
| YOLOF-R50-C5_1x | 800,1333 | 50 | 37.5 | 57.4 | github |
| YOLOF-R50-DC5_1x | 800,1333 | 32 | 38.7 | 58.5 | github |
| YOLOF-R101-C5_1x | 800,1333 | github | |||
| YOLOF-R101-DC5_1x | 800,1333 | github | |||
| YOLOF-RT-R50_3x | 512,736 | 60 | 39.4 | 58.6 | github |
Limited by my computing resources, I cannot train YOLOF_R101_C5_1x, YOLOF_R101_DC5_1x.
I would be very grateful if you used this project to train them and would like to share weight files.
- Visualization
(YOLOF_R50-C5_1x)
- AP results of FCOS
| Model | scale | FPS | AP | AP50 | Weight |
|---|---|---|---|---|---|
| FCOS-R18_1x | 800,1333 | 42 | 33.0 | 51.3 | github |
| FCOS-R50_1x | 800,1333 | 30 | 38.2 | 58.0 | github |
| FCOS-RT-R18_4x | 512,736 | 83 | 33.8 | 51.5 | github |
| FCOS-RT-R50_4x | 512,736 | 60 | 38.7 | 58.0 | github |
| FCOS-RT-R18-OTA_4x | 512,736 | ||||
| FCOS-RT-R50-OTA_4x | 512,736 |
FCOS-RT-R18-OTA_4x means that we use the SimOTA to train the FCOS-RT-R18.
- AP results of RetiniaNet
| Model | scale | FPS | AP | AP50 | Weight |
|---|---|---|---|---|---|
| RetinaNet-R18_1x | 800,1333 | 30.8 | 49.6 | github | |
| RetinaNet-R50_1x | 800,1333 | ||||
| RetinaNet-RT-R18_4x | 512,736 | ||||
| RetinaNet-RT-R50_4x | 512,736 |
- Single GPU
You can run the following command:
python train.py \
--cuda \
-d coco \
--root /mnt/share/ssd2/dataset/ \
-v yolof-r50 \
--batch_size 16 \
--schedule 1x \
--grad_clip_norm 4.0 \or, you just run the script:
sh train.shYou can change the configurations of train.sh, according to your own situation.
- Multi GPUs
You can run the following command:
# 2 GPUs
python -m torch.distributed.run --nproc_per_node=2 train.py \
--cuda \
-dist \
-d coco \
--root /mnt/share/ssd2/dataset/ \
-v yolof-r50 \
--batch_size 8 \
--grad_clip_norm 4.0 \
--num_workers 4 \
--schedule 1x \or, you just run the script:
sh train_ddp.shpython test.py -d coco \
--cuda \
-v yolof-r50 \
--weight path/to/weight \
--root path/to/dataset/ \
--showpython eval.py -d coco-val \
--cuda \
-v yolof-r50 \
--weight path/to/weight \
--root path/to/dataset/ \Our AP results of YOLOF-R50-C5-1x on COCO-val:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.375
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.574
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.399
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.312
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.507
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.552
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.739I 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 yolof-r50 \
--cuda \
--weight path/to/weight \
--showIf 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 yolof-r50 \
--cuda \
--weight path/to/weight \
--showIf you want run video detection with your camera, you need to set --mode to camera。
python demo.py --mode camera \
-v yolof-r50 \
--cuda \
--weight path/to/weight \
--show