tf2-keras-yolo3

行吧行吧，我知道这个项目真的很不好用~

那么，来看看 xyolo 吧！

xyolo 是对 tf2-keras-yolo3的重构和封装，旨在降低使用门槛，帮助实现快速开发。

几行Python代码即可训练自己的目标检测模型或者调用模型进行检测哦~你不试试吗？

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这是对qqwweee/keras-yolo3的fork和修改，目的是使它支持TensorFlow 2.2。

主要修改内容如下：

• 以tf.keras为主导，替换掉独立的keras库
• 修改部分基于TensorFlow 1.x版本的接口和逻辑，使项目支持TensorFlow 2.2
• 修改原项目命令行参数错误

2020.6.29 更新：

• 在TensorFlow 2.2下测试兼容性，运行正常
• 之前有朋友反映无法通过train.py完成自定义数据集的训练，我在TensorFlow 2.2下做了测试，一切正常
• 使用tf.function优化模型性能

关于训练：

亲测TensorFlow 2.2下训练自定义数据集是没有问题的，训练不成功的同学请尝试如下方法：

1. 升级TensorFlow版本为2.2
2. 仔细阅读原README.MD中关于train.py部分的表述，在训练前需要先准备数据集、处理数据格式以及按实际情况修改train.py中的参数。如果没有做这些工作的话，出现错误很正常。

TODO:

• 编写一个使用tf2-keras-yolo3训练自己的数据集的详细教程。
• 提取各脚本中常用配置参数到统一文件

更多信息请访问 深度学习下的目标检测算法——TensorFlow 2.0下的YOLOv3实践 (https://blog.csdn.net/aaronjny/article/details/103658254)

下附qqwweee/keras-yolo3的README.

This is a fork and modification of qqwweee / keras-yolo3, in order to make it support TensorFlow 2.2.

Attached is the README of qqwweee / keras-yolo3.

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keras-yolo3

Introduction

A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.

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Quick Start

1. Download YOLOv3 weights from YOLO website.
2. Convert the Darknet YOLO model to a Keras model.
3. Run YOLO detection.

wget https://pjreddie.com/media/files/yolov3.weights
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
python yolo_video.py [OPTIONS...] --image, for image detection mode, OR
python yolo_video.py [video_path] [output_path (optional)]

For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with --model model_file and --anchors anchor_file.

Usage

Use --help to see usage of yolo_video.py:

usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
                     [--classes CLASSES] [--gpu_num GPU_NUM] [--image]
                     [--input] [--output]

positional arguments:
  --input        Video input path
  --output       Video output path

optional arguments:
  -h, --help         show this help message and exit
  --model MODEL      path to model weight file, default model_data/yolo.h5
  --anchors ANCHORS  path to anchor definitions, default
                     model_data/yolo_anchors.txt
  --classes CLASSES  path to class definitions, default
                     model_data/coco_classes.txt
  --gpu_num GPU_NUM  Number of GPU to use, default 1
  --image            Image detection mode, will ignore all positional arguments

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4. MultiGPU usage: use --gpu_num N to use N GPUs. It is passed to the Keras multi_gpu_model().

Training

1. Generate your own annotation file and class names file.
   One row for one image;
   Row format: image_file_path box1 box2 ... boxN;
   Box format: x_min,y_min,x_max,y_max,class_id (no space).
   For VOC dataset, try python voc_annotation.py
   Here is an example:

   path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
   path/to/img2.jpg 120,300,250,600,2
   ...
   

2. Make sure you have run python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
   The file model_data/yolo_weights.h5 is used to load pretrained weights.

3. Modify train.py and start training.
   python train.py
   Use your trained weights or checkpoint weights with command line option --model model_file when using yolo_video.py Remember to modify class path or anchor path, with --classes class_file and --anchors anchor_file.

If you want to use original pretrained weights for YOLOv3:
1. wget https://pjreddie.com/media/files/darknet53.conv.74
2. rename it as darknet53.weights
3. python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5
4. use model_data/darknet53_weights.h5 in train.py

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Some issues to know

1. The test environment is

   • Python 3.5.2
   • Keras 2.1.5
   • tensorflow 1.6.0

2. Default anchors are used. If you use your own anchors, probably some changes are needed.

3. The inference result is not totally the same as Darknet but the difference is small.

4. The speed is slower than Darknet. Replacing PIL with opencv may help a little.

5. Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning.

6. The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed.

7. For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See this for more information on bottleneck features.