# keras-yolo3
[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE)
## Introduction
A Keras implementation of YOLOv3 (Tensorflow backend) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K).
---
## Quick Start
1. Download YOLOv3 weights from [YOLO website](http://pjreddie.com/darknet/yolo/).
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
```
---
4. MultiGPU usage: use `--gpu_num N` to use N GPUs. It is passed to the [Keras multi_gpu_model()](https://keras.io/utils/#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
---
## 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](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) for more information on bottleneck features.
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本科毕业设计内容,临时仓库.zip (54个子文件)
Graduation Design
voc_annotation.py 1KB
train_sample.txt 16KB
yolo_video.py 2KB
draw_bbox.py 3KB
data_augmentation.py 18KB
2007_val.txt 265KB
train04.txt 39KB
font
FiraMono-Medium.otf 124KB
SIL Open Font License.txt 4KB
train02.txt 46KB
train05.txt 17KB
LICENSE 1KB
coco_annotation.py 1KB
model_data
train04_classes.txt 67B
yolo_anchors.txt 75B
train05_classes.txt 24B
train12_anchors.txt 77B
train02_classes.txt 79B
train06_classes.txt 24B
train06_anchors.txt 76B
train05_anchors.txt 77B
train04_anchors.txt 77B
train07_anchors.txt 75B
train02_anchors.txt 76B
coco_classes.txt 624B
tiny_yolo_anchors.txt 50B
train01_anchors.txt 77B
voc_classes.txt 134B
train01.txt 16KB
convert.py 10KB
2007_test.txt 515KB
train07.py 10KB
2007_train.txt 264KB
train06.txt 149KB
darknet53.cfg 6KB
train08.py 9KB
test.txt 1KB
yolov3.cfg 8KB
get_annotation.py 1KB
.gitignore 1KB
yolov3-tiny.cfg 2KB
train12.py 10KB
train09.py 9KB
kmeans.py 3KB
train_bottleneck.py 10KB
train.py 10KB
train10.py 10KB
train07.txt 700KB
yolo3
utils.py 4KB
__init__.py 0B
model.py 16KB
README.md 4KB
train12.txt 1.37MB
yolo.py 8KB
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