Minimal PyTorch implementation of YOLOv4,out-of-the-box,training,testing&detect your custom objects
fork from https://github.com/eriklindernoren/PyTorch-YOLOv3 & https://github.com/Python3WebSpider/DeepLearningSlideCaptcha
git clone https://github.com.cnpmjs.org/SOVLOOKUP/PyTorch-YOLOv4
cd PyTorch-YOLOv4/
sudo pip3 install -r requirements.txt
$ cd weights/ # Navigate to weights dir
$ bash download_weights.sh # Will download pretrain models
it is not necessary
I use an another dataset to test yolov4 but if you want test it on coco you can download it.
$ cd data/ # Navigate to data dir
$ bash get_coco_dataset.sh # Will download coco dataset
Use LabelImg to mark your datas in browser.
LabelImg：https://github.com/tzutalin/labelImg
Or you can use Yolomark.
Yolomark: https://github.com/AlexeyAB/Yolo_mark
Slider verification data set has been prepared in <data/captcha>.
training command：
python3 train.py --model_def config/yolov4-captcha.cfg --data_config config/captcha.data --pretrained_weights weights/pre/yolov4.conv.137
Track training progress in Tensorboard:
- Initialize training
- Run the command below
- Go to http://localhost:6006/
$ tensorboard --logdir='logs' --port=6006
After the training, the PTH file will be generated in the <checkpoints> folder, and the model can be directly used to predict the results.
testing command：
python3 detect.py --model_def config/yolov4-captcha.cfg --weights_path checkpoints/yolov4_ckpt.pth --image_folder data/captcha/test --class_path data/captcha/classes.names
The script will read all the pictures under <data/captcha/test>, and output the processed results to the <data/captcha/result> folder.
OutPut：
Performing object detection:
+ Batch 0, Inference Time: 0:00:00.044223
+ Batch 1, Inference Time: 0:00:00.028566
+ Batch 2, Inference Time: 0:00:00.029764
+ Batch 3, Inference Time: 0:00:00.032430
+ Batch 4, Inference Time: 0:00:00.033373
+ Batch 5, Inference Time: 0:00:00.027861
+ Batch 6, Inference Time: 0:00:00.031444
+ Batch 7, Inference Time: 0:00:00.032110
+ Batch 8, Inference Time: 0:00:00.029131
Saving images:
(0) Image: 'data/captcha/test/captcha_4497.png'
+ Label: target, Conf: 0.99999
(1) Image: 'data/captcha/test/captcha_4498.png'
+ Label: target, Conf: 0.99999
(2) Image: 'data/captcha/test/captcha_4499.png'
+ Label: target, Conf: 0.99997
(3) Image: 'data/captcha/test/captcha_4500.png'
+ Label: target, Conf: 0.99999
(4) Image: 'data/captcha/test/captcha_4501.png'
+ Label: target, Conf: 0.99997
(5) Image: 'data/captcha/test/captcha_4502.png'
+ Label: target, Conf: 0.99999
(6) Image: 'data/captcha/test/captcha_4503.png'
+ Label: target, Conf: 0.99997
(7) Image: 'data/captcha/test/captcha_4504.png'
+ Label: target, Conf: 0.99998
(8) Image: 'data/captcha/test/captcha_4505.png'
+ Label: target, Conf: 0.99998
Results：
-
format your data：https://github.com/eriklindernoren/PyTorch-YOLOv3#train-on-custom-dataset。
-
create your model-config:
replace <num-classes> with your num-classes
$ cd config/ # Navigate to config dir
$ bash create_custom_model.sh <num-classes> # Will create custom model 'yolov4-custom.cfg'
- train it training command：
python3 train.py --model_def config/yolov4-custom.cfg --data_config config/{YourSetName}.data --pretrained_weights weights/pre/yolov4.conv.137
本项目基于开源 GNU 协议，另外本项目不提供任何有关滑动轨迹相关模拟和 JavaScript 逆向分析方案。
本项目仅供学习交流使用，请勿用于非法用途，本人不承担任何法律责任。
如有侵权请联系个人删除，谢谢。
@article{yolov4,
title={YOLOv4: YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao},
journal = {arXiv},
year={2020}
}
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