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https://github.com/YuHengsss/YOLOV
This repo is an implementation of PyTorch version YOLOV Series
https://github.com/YuHengsss/YOLOV
Last synced: 12 days ago
JSON representation
This repo is an implementation of PyTorch version YOLOV Series
- Host: GitHub
- URL: https://github.com/YuHengsss/YOLOV
- Owner: YuHengsss
- License: apache-2.0
- Created: 2022-08-20T10:41:40.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-20T03:11:40.000Z (about 1 month ago)
- Last Synced: 2024-05-23T06:32:41.430Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 9.07 MB
- Stars: 265
- Watchers: 6
- Forks: 39
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - YOLOV
- awesome-video-object-detection - YOLOV
- awesome-stars - YuHengsss/YOLOV - This repo is an implementation of PyTorch version YOLOV Series (Python)
README
# YOLOV and YOLOV++ for video object detection.
## Update
* **` April. 21th, 2024`**: Our enhanced model now achieves a 92.9 AP50(w.o post-processing) on the ImageNet VID dataset, thanks to a more robust backbone and algorithm improvements. It maintains a processing time of 26.5ms per image during batch inference on a 3090 GPU. Code release is forthcoming.
* **` May. 8th, 2024`**: We release code, log and weights for YOLOV++.
## Introduction
[](https://paperswithcode.com/sota/video-object-detection-on-imagenet-vid?p=yolov-making-still-image-object-detectors)
YOLOV series are high performance video object detector. Please refer to [YOLOV](https://arxiv.org/abs/2208.09686) on Arxiv for more details. Paper for YOLOV++ will be released soon.
This repo is an implementation of PyTorch version YOLOV and YOLOV++ based on [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX).
## YOLOX Pretain Models on ImageNet VID
| Model | size | mAP@50val
| Speed 2080Ti(batch size=1)
(ms) | Speed 3090(batch size=32)
(ms) | weights |
|------------------|:----:|:------------------:|:----------------------------------:|:---------------------------------:|:------------------------------------------------------------------------------------------------:|
| YOLOX-s | 576 | 69.5 | 9.4 | 1.4 | [google](https://drive.google.com/file/d/1n8wkByqpHdrGy6z9fsoZpBtTa0I3JOcG/view?usp=sharing) |
| YOLOX-l | 576 | 76.1 | 14.8 | 4.2 | [google](https://drive.google.com/file/d/1rikaPCAHBBIugYUZYV1buyOIRG8xvGKB/view?usp=sharing) |
| YOLOX-x | 576 | 77.8 | 20.4 | - | [google](https://drive.google.com/file/d/1OH3hGj7RMfcinMKPESbfI7C5y_RrA3aF/view?usp=sharing) |
| YOLOX-SwinTiny | 576 | 79.2 | 19.0 | 5.5 |[google](https://drive.google.com/file/d/1s1gKLXMX5Hwxkx7e9nZyzJ1oF9iPvEe1/view?usp=drive_link) |
| YOLOX-SwinBase | 576 | 86.5 | 24.9 | 11.8 |[google](https://drive.google.com/drive/folders/1K5897iM2zzN4kcj8qdK3z_FtvW9f3kHN?usp=drive_link) |
| YOLOX-FocalLarge | 576 | 89.7 | 42.2 | 25.7 | - |
## Main result in YOLOV++
| Model | size | mAP@50val
| Speed 3090(batch size=32)
(ms) | weights | logs |
|---------------------------|:----:|:------------------:|:---------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|-----------------------------------------------------------------------------------------------|
| YOLOV++ s | 576 | 78.7 | 5.3 | [google](https://drive.google.com/file/d/1vlFlwyoRoo_qS2CkfTZE5iQ32MDoA1n4/view?usp=drive_link) | [link](https://drive.google.com/file/d/1wIA71zsNxAtDflPGxLTzrRDdKy0Zl1HZ/view?usp=drive_link) |
| YOLOV++ l | 576 | 84.2 | 7.6 | [google](https://drive.google.com/file/d/1qb_abseRfOmRr8IiOuUSAlCUrBvUhdim/view?usp=drive_link) | - |
| YOLOV++ SwinTiny | 576 | 85.6 | 8.4 | [google](https://drive.google.com/file/d/1pCIWAK6cy-BHhDVywmPb1LuuQHzNXdT2/view?usp=drive_link) | [link](https://drive.google.com/file/d/1RmY0LW1sUil6WilvNq2hW1a4obw27531/view?usp=drive_link) |
| YOLOV++ SwinBase | 576 | 90.7 | 15.9 | [google](https://drive.google.com/file/d/1RGb499EBcSQjWDxu6KkvN4Tr1wSc6SHb/view?usp=drive_link) | [link](https://drive.google.com/file/d/10qGMScfy0BvmqSMLuTGRPRlZxqkNZ9GX/view?usp=drive_link) |
| YOLOV++ FocalLarge | 576 | 92.9 | 27.6 | [google](https://drive.google.com/file/d/11WT_GcZU7HHjWV4i9KoXHhh70zneraEE/view?usp=drive_link) | [link](https://drive.google.com/file/d/1QvU9pvNLZF2_jrshEqx-Pryb14ayfGw7/view?usp=drive_link) |
| YOLOV++ FocalLarge + Post | 576 | 93.2 | - | - | |
## Main result in YOLOV
| Model | size | mAP@50val
| Speed 2080Ti(batch size=1)
(ms) | weights |
|---------------------------------------------------------------------------------------------------------------------|:----:|:------------------:|:----------------------------------:|:--------------------------------------------------------------------------------------------:|
| YOLOV-s | 576 | 77.3 | 11.3 | [google](https://drive.google.com/file/d/12X4dQw45aXVYgJjKAAAPk409FO3xValW/view?usp=sharing) |
| YOLOV-l | 576 | 83.6 | 16.4 | [google](https://drive.google.com/file/d/1qZ-3iPDlYx1OKe6zz_-n42ceijo_Ntx6/view?usp=sharing) |
| YOLOV-x | 576 | 85.5 | 22.7 | [google](https://drive.google.com/file/d/1OIozS-D9wbWA9pDFl5xoFw6XqEcYtzsJ/view?usp=sharing) |
| YOLOV-x + [post](https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection) | 576 | 87.5 | - | - |
## TODO
- [x] Finish Swin-Transformer based experiments.
- [ ] Release updated code, model and log.
## Quick Start
Installation
Install YOLOV from source.
```shell
git clone [email protected]:YuHengsss/YOLOV.git
cd YOLOV
```
Create conda env.
```shell
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:
```shell
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:
```shell
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](https://image-net.org/challenges/LSVRC/2015/2015-downloads) and organise them as follows:
```shell
path to your datasets/ILSVRC2015/
path to your datasets/ILSVRC/
```
Download our COCO-style annotations for [training](https://drive.google.com/file/d/1HhE4OAcc--CpjUj69JCRXzMvIRsR4ymM/view?usp=sharing), FGFA version training [annotation](https://drive.google.com/file/d/12ceMTsmwkCMCdjYSM268qYfQTQcCDYFU/view?usp=drive_link) and [video sequences](https://drive.google.com/file/d/1vJs8rLl_2oZOWCMJtk3a9ZJmdNn8cu-G/view?usp=sharing). Then, put them in these two directories:
```shell
YOLOV/annotations/vid_train_coco.json
YOLOV/annotations/ILSVRC_FGFA_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.
```shell
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:
```shell
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:
```shell
{'mAP_total': 0.8758871720817065, 'mAP_slow': 0.9059275666099181, 'mAP_medium': 0.8691557352372217, 'mAP_fast': 0.7459511040452989}
```
**Training example**
```shell
python tools/vid_train.py -f exps/yolov/yolov_s.py -c weights/yoloxs_vid.pth --fp16
```
**Roughly testing**
```shell
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
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 Series**
The train_seq.npy and val_seq.npy files are numpy arrays of lists. They can be loaded using the following command:
```shell
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 https://github.com/YuHengsss/YOLOV/blob/f5a57ddea2f3660875d6d75fc5fa2ddbb95028a7/yolox/data/datasets/vid.py#L125 for more details.
## Acknowledgements
Expand
* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
* [https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection](https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection)
## Cite YOLOV and YOLOV++
If YOLOV series are helpful for your research, please cite the following paper:
```latex
@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}
}
```