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- We recommend you to use Anaconda to create a conda environment:
conda create -n rtcdet python=3.6
- Then, activate the environment:
conda activate rtcdet
- Requirements:
pip install -r requirements.txt
My environment:
- PyTorch = 1.9.1
- Torchvision = 0.10.1
At least, please make sure your torch is version 1.x.
- Download VOC.
cd <RT-ODLab>
cd dataset/scripts/
sh VOC2007.sh
sh VOC2012.sh
- Check VOC
cd <RT-ODLab>
python dataset/voc.py
- Train on VOC
For example:
python train.py --cuda -d voc --root path/to/VOCdevkit -m yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
- Download COCO.
cd <RT-ODLab>
cd dataset/scripts/
sh COCO2017.sh
- Check COCO
cd <RT-ODLab>
python dataset/coco.py
- Train on COCO
For example:
python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
sh train_single_gpu.sh
You can change the configurations of train_single_gpu.sh
, according to your own situation.
You also can add --vis_tgt
to check the images and targets during the training stage. For example:
python train.py --cuda -d coco --root path/to/coco -m yolov1 --vis_tgt
sh train_multi_gpus.sh
You can change the configurations of train_multi_gpus.sh
, according to your own situation.
In the event of a training interruption, you can pass --resume
the latest training
weight path (None
by default) to resume training. For example:
python train.py \
--cuda \
-d coco \
-m yolov1 \
-bs 16 \
--max_epoch 300 \
--wp_epoch 3 \
--eval_epoch 10 \
--ema \
--fp16 \
--resume weights/coco/yolov1/yolov1_epoch_151_39.24.pth
Then, training will continue from 151 epoch.
python test.py -d coco \
--cuda \
-m yolov1 \
--img_size 640 \
--weight path/to/weight \
--root path/to/dataset/ \
--show
For YOLOv7, since it uses the RepConv in PaFPN, you can add --fuse_repconv
to fuse the RepConv block.
python test.py -d coco \
--cuda \
-m yolov7_large \
--fuse_repconv \
--img_size 640 \
--weight path/to/weight \
--root path/to/dataset/ \
--show
python eval.py -d coco-val \
--cuda \
-m yolov1 \
--img_size 640 \
--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/ \
--cuda \
--img_size 640 \
-m yolov2 \
--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_vid data/demo/videos/your_video \
--cuda \
--img_size 640 \
-m yolov2 \
--weight path/to/weight \
--show \
--gif
If you want run video detection with your camera, you need to set --mode
to camera
。
python demo.py --mode camera \
--cuda \
--img_size 640 \
-m yolov2 \
--weight path/to/weight \
--show \
--gif
- Detector: YOLOv2
Command:
python demo.py --mode video \
--path_to_vid ./dataset/demo/videos/000006.mp4 \
--cuda \
--img_size 640 \
-m yolov2 \
--weight path/to/weight \
--show \
--gif
Results:
Our project also supports multi-object tracking tasks. We use the YOLO of this project as the detector, following the "tracking-by-detection" framework, and use the simple and efficient ByteTrack as the tracker.
- images tracking
python track.py --mode image \
--path_to_img path/to/images/ \
--cuda \
-size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/coco_pretrained/ \
--show \
--gif
- video tracking
python track.py --mode video \
--path_to_img path/to/video/ \
--cuda \
-size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/coco_pretrained/ \
--show \
--gif
- camera tracking
python track.py --mode camera \
--cuda \
-size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/coco_pretrained/ \
--show \
--gif
- Detector: YOLOv2
- Tracker: ByteTracker
- Device: i5-12500H CPU
Command:
python track.py --mode video \
--path_to_img ./dataset/demo/videos/000006.mp4 \
-size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/coco_pretrained/ \
--show \
--gif
Results:
Besides the popular datasets, we can also train the model on ourself dataset. To achieve this goal, you should follow these steps:
- Step-1: Prepare the images (JPG/JPEG/PNG ...) and use
labelimg
to make XML format annotation files.
OurDataset
|_ train
| |_ images
| |_ 0.jpg
| |_ 1.jpg
| |_ ...
| |_ annotations
| |_ 0.xml
| |_ 1.xml
| |_ ...
|_ val
| |_ images
| |_ 0.jpg
| |_ 1.jpg
| |_ ...
| |_ annotations
| |_ 0.xml
| |_ 1.xml
| |_ ...
| ...
- Step-2: Convert ourdataset to COCO format.
cd <PyTorch_YOLO_Tutorial_HOME>
cd tools
# convert train split
python convert_ours_to_coco.py --root path/to/dataset/ --split train
# convert val split
python convert_ours_to_coco.py --root path/to/dataset/ --split val
Then, we can get a train.json
file and a val.json
file, as shown below.
OurDataset
|_ train
| |_ images
| |_ 0.jpg
| |_ 1.jpg
| |_ ...
| |_ annotations
| |_ 0.xml
| |_ 1.xml
| |_ ...
| |_ train.json
|_ val
| |_ images
| |_ 0.jpg
| |_ 1.jpg
| |_ ...
| |_ annotations
| |_ 0.xml
| |_ 1.xml
| |_ ...
| |_ val.json
| ...
- Step-3 Define our class labels.
Please open dataset/ourdataset.py
file and change our_class_labels = ('cat',)
according to our definition of categories.
- Step-4 Check
cd <PyTorch_YOLO_Tutorial_HOME>
cd dataset
# convert train split
python ourdataset.py --root path/to/dataset/ --split train
# convert val split
python ourdataset.py --root path/to/dataset/ --split val
- Step-5 Train
For example:
cd <PyTorch_YOLO_Tutorial_HOME>
python train.py --root path/to/dataset/ -d ourdataset -m yolov1 -bs 16 --max_epoch 100 --wp_epoch 1 --eval_epoch 5 -p path/to/yolov1_coco.pth
- Step-6 Test
For example:
cd <PyTorch_YOLO_Tutorial_HOME>
python test.py --root path/to/dataset/ -d ourdataset -m yolov1 --weight path/to/checkpoint --show
- Step-7 Eval
For example:
cd <PyTorch_YOLO_Tutorial_HOME>
python eval.py --root path/to/dataset/ -d ourdataset -m yolov1 --weight path/to/checkpoint