PyTorch-YOLOv3

Minimal implementation of YOLOv3 in PyTorch.
models.py和parse_config.py共同提供了将配置文件yolov3.cfg组装成模型的功能
utils.py提供了nms, iou, 权重随机初始化(用于训练)等功能函数
detect.py提供了基于核心模块的包装功能, 可以执行目标检测任务
train.py提供了训练功能, data目录下的shell提供了从coco数据集下载目标数据的功能
test.py提供了验证train.py训练结果的功能

PyTorch-YOLOv3
- Table of Contents
- Paper
- Installation
- Inference
- Test
- Train
- Credit

Paper

YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

Abstract
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though, don’t worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5 AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is online at https://pjreddie.com/yolo/.

[Paper] [Original Implementation]

Installation

$ git clone https://github.com/eriklindernoren/PyTorch-YOLOv3
$ cd PyTorch-YOLOv3/
$ sudo pip3 install -r requirements.txt

Download pretrained weights

$ cd weights/
$ bash download_weights.sh

Download COCO

$ cd data/
$ bash get_coco_dataset.sh

Inference

Uses pretrained weights to make predictions on images. Below table displays the inference times when using as inputs images scaled to 256x256. The ResNet backbone measurements are taken from the YOLOv3 paper. The Darknet-53 measurement marked shows the inference time of this implementation on my 1080ti card.

Backbone	GPU	FPS
ResNet-101	Titan X	53
ResNet-152	Titan X	37
Darknet-53 (paper)	Titan X	76
Darknet-53 (this impl.)	1080ti	74

$ python3 detect.py --image_folder /data/samples

Test

Evaluates the model on COCO test.

$ python3 test.py --weights_path weights/yolov3.weights

Model	mAP (min. 50 IoU)
YOLOv3 (paper)	57.9
YOLOv3 (this impl.)	58.2

Train

Data augmentation as well as additional training tricks remains to be implemented. PRs are welcomed!

    train.py [-h] [--epochs EPOCHS] [--image_folder IMAGE_FOLDER]
                [--batch_size BATCH_SIZE]
                [--model_config_path MODEL_CONFIG_PATH]
                [--data_config_path DATA_CONFIG_PATH]
                [--weights_path WEIGHTS_PATH] [--class_path CLASS_PATH]
                [--conf_thres CONF_THRES] [--nms_thres NMS_THRES]
                [--n_cpu N_CPU] [--img_size IMG_SIZE]
                [--checkpoint_interval CHECKPOINT_INTERVAL]
                [--checkpoint_dir CHECKPOINT_DIR]

Credit

@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}

Bingo0408/PyTorch-yolov3-with-train