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We are trying to build our real-time general target detection code base based on the core concepts of YOLO. We have reproduced most of the YOLO series. In addition, we have also written an introductory tutorial on YOLO. We hope that by learning YOLO, a very popular general target detection framework, beginners can master the basic knowledge necessary to study general target detection.
If you are interested in our book, you can purchase it on e-commerce platforms such as Taobao and JD.com in China.
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
cd ./ppdet/modeling/transformers/ext_op/
python setup_ms_deformable_attn_op.py install
See details
My environment:
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
Clean COCO
cd <RT-ODLab>
cd tools/
python clean_coco.py --root path/to/coco --image_set val
python clean_coco.py --root path/to/coco --image_set train
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
We also kindly provide a script train.sh
to run the training code. You need to follow the following format to use this script:
bash train.sh <model> <data> <data_path> <batch_size> <num_gpus> <master_port> <resume_weight>
For example, we use this script to train YOLOv3 from the epoch-0:
bash train.sh yolov3 coco path/to/coco 128 4 1699 None
We can also continue training from existing weights by passing the model's weight file to the resume parameter.
bash train.sh yolov3 coco path/to/coco 128 4 1699 path/to/yolov3.pth
python test.py --cuda --dataset coco --root path/to/coco --model yolov1 --weight path/to/yolov1.pth --img_size 640 --show
python eval.py --cuda --dataset coco --root path/to/coco --model yolov1 --weight path/to/yolov1.pth --img_size 640
I have provide some images in data/demo/images/
, so you can run following command to run a demo with coco pretrained model:
python demo.py --mode image \
--path_to_img data/demo/images/ \
--dataset coco \
--cuda \
--img_size 640 \
--model yolov2 \
--weight path/to/yolov2_coco.pth \
--show
If you want to try this command with voc pretrained model, you could refer to the following command:
python demo.py --mode image \
--path_to_img data/demo/images/ \
--dataset voc \
--cuda \
--img_size 640 \
--model yolov2 \
--weight path/to/yolov2_voc.pth \
--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 \
--dataset coco \
--cuda \
--img_size 640 \
--model yolov2 \
--weight path/to/yolov2_coco.pth \
--show \
--gif
If you want run video detection with your camera, you need to set --mode
to camera
。
python demo.py --mode camera \
--cuda \
--dataset coco \
--img_size 640 \
--model yolov2 \
--weight path/to/yolov2_coco.pth \
--show \
--gif
Command:
python demo.py --mode video \
--path_to_vid ./dataset/demo/videos/000006.mp4 \
--cuda \
--dataset coco \
--img_size 640 \
--model yolov2 \
--weight path/to/yolov2_coco.pth \
--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 \
--img_size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/yolov2_coco.pth \
--show \
--gif
video tracking
python track.py --mode video \
--path_to_img path/to/video/ \
--cuda \
--img_size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/yolov2_coco.pth \
--show \
--gif
python track.py --mode camera \
--cuda \
--img_size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/yolov2_coco.pth \
--show \
--gif
Command:
python track.py --mode video \
--path_to_img ./dataset/demo/videos/000006.mp4 \
--img_size 640 \
-dt yolov2 \
-tk byte_tracker \
--weight path/to/yolov2_coco.pth \
--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:
labelimg
to make XML format annotation files.CustomedDataset
|_ train
| |_ images
| |_ 0.jpg
| |_ 1.jpg
| |_ ...
| |_ annotations
| |_ 0.xml
| |_ 1.xml
| |_ ...
|_ val
| |_ images
| |_ 0.jpg
| |_ 1.jpg
| |_ ...
| |_ annotations
| |_ 0.xml
| |_ 1.xml
| |_ ...
| ...
cd <RT-ODLab>
cd config/data_config
You need to edit the dataset_cfg
defined in dataset_config.py
. You can refer to the customed
defined in dataset_cfg
to modify the relevant parameters, such as num_classes
, classes_names
, to adapt to our dataset.
For example:
dataset_cfg = {
'customed':{
'data_name': 'AnimalDataset',
'num_classes': 9,
'class_indexs': (0, 1, 2, 3, 4, 5, 6, 7, 8),
'class_names': ('bird', 'butterfly', 'cat', 'cow', 'dog', 'lion', 'person', 'pig', 'tiger', ),
},
}
cd <RT-ODLab>
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.
CustomedDataset
|_ 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
| ...
cd <RT-ODLab>
cd dataset
# convert train split
python customed.py --root path/to/dataset/ --split train
# convert val split
python customed.py --root path/to/dataset/ --split val
For example:
cd <RT-ODLab>
python train.py --root path/to/dataset/ -d customed -m yolov1 -bs 16 --max_epoch 100 --wp_epoch 1 --eval_epoch 5 -p path/to/yolov1_coco.pth
For example:
cd <RT-ODLab>
python test.py --root path/to/dataset/ -d customed -m yolov1 --weight path/to/checkpoint --show
For example:
cd <RT-ODLab>
python eval.py --root path/to/dataset/ -d customed -m yolov1 --weight path/to/checkpoint