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+# 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.