YOLOV for video object detection.

Introduction

YOLOV is a high performance video object detector. Please refer to our paper on Arxiv for more details.

This repo is an implementation of PyTorch version YOLOV based on YOLOX.

Main result

Model	size	mAP@50val	Speed 2080Ti(batch size=1) (ms)	weights
YOLOX-s	576	69.5	9.4	google
YOLOX-l	576	76.1	14.8	google
YOLOX-x	576	77.8	20.4	google
YOLOV-s	576	77.3	11.3	google
YOLOV-l	576	83.6	16.4	google
YOLOV-x	576	85.5	22.7	google
YOLOV-x + post	576	87.5	-	-

TODO

Finish Swin-Transformer based experiments.

Quick Start

Installation

Install YOLOV from source.

git clone git@github.com:YuHengsss/YOLOV.git
cd YOLOV

Create conda env.

conda create -n yolov python=3.7

conda activate yolov

pip install -r requirements.txt

pip3 install -v -e .

Demo

Step1. Download a pretrained weights.

Step2. Run yolov demos. For example:

python tools/vid_demo.py -f [path to your yolov exp files] -c [path to your yolov weights] --path /path/to/your/video --conf 0.25 --nms 0.5 --tsize 576 --save_result 

For online mode, exampled with yolov_l, you can run:

python tools/yolov_demo_online.py -f ./exp/yolov/yolov_l_online.py -c [path to your weights] --path /path/to/your/video --conf 0.25 --nms 0.5 --tsize 576 --save_result 

For yolox models, please use python tools/demo.py for inferencing.

Reproduce our results on VID

Step1. Download datasets and weights:

Download ILSVRC2015 DET and ILSVRC2015 VID dataset from IMAGENET and organise them as follows:

path to your datasets/ILSVRC2015/
path to your datasets/ILSVRC/

Download our COCO-style annotations for training and video sequences. Then, put them in these two directories:

YOLOV/annotations/vid_train_coco.json
YOLOV/yolox/data/dataset/train_seq.npy

Change the data_dir in exp files to [path to your datasets] and Download our weights.

Step2. Generate predictions and convert them to IMDB style for evaluation.

python tools/val_to_imdb.py -f exps/yolov/yolov_x.py -c path to your weights/yolov_x.pth --fp16 --output_dir ./yolov_x.pkl

Evaluation process:

python tools/REPPM.py --repp_cfg ./tools/yolo_repp_cfg.json --predictions_file ./yolov_x.pkl --evaluate --annotations_filename ./annotations/annotations_val_ILSVRC.txt --path_dataset [path to your dataset] --store_imdb --store_coco  (--post)

(--post) indicates involving post-processing method. Then you will get:

{'mAP_total': 0.8758871720817065, 'mAP_slow': 0.9059275666099181, 'mAP_medium': 0.8691557352372217, 'mAP_fast': 0.7459511040452989}

Training example

python tools/vid_train.py -f exps/yolov/yolov_s.py -c weights/yoloxs_vid.pth --fp16

Roughly testing

python tools/vid_eval.py -f exps/yolov/yolov_s.py -c weights/yolov_s.pth --tnum 500 --fp16

tnum indicates testing sequence number.

Annotation format in YOLOV

Details

Training base detector

The train_coco.json is a COCO format annotation file. When trainig the base detector on your own dataset, try to convert the annotation to COCO format.

Training YOLOV

The train_seq.npy and val_seq.npy files are numpy arrays of lists. They can be loaded using the following command:

numpy.load('./yolox/data/datasets/train_seq.npy',allow_pickle=True)

Each list contains the paths to all images in a video. The specific annotations(xml annotation in VID dataset) are loaded via these image paths, refer to

YOLOV/yolox/data/datasets/vid.py

Line 125 in f5a57dd

def get_annotation(self,path,test_size):

for more details.

Acknowledgements

Expand

• https://github.com/Megvii-BaseDetection/YOLOX
• https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection

Cite YOLOV

If YOLOV is helpful for your research, please cite the following paper:

@article{shi2022yolov,
  title={YOLOV: Making Still Image Object Detectors Great at Video Object Detection},
  author={Shi, Yuheng and Wang, Naiyan and Guo, Xiaojie},
  journal={arXiv preprint arXiv:2208.09686},
  year={2022}
}