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https://github.com/YunYang1994/tensorflow-yolov3

🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"
https://github.com/YunYang1994/tensorflow-yolov3

deep-learning object-detection tensorflow yolov3

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🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

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README 

        

## 🆕 Are you looking for a new YOLOv3 implemented by TF2.0 ?



>If you hate the fucking tensorflow1.x very much, no worries! I have implemented **a new YOLOv3 repo with TF2.0**, and also made a chinese blog on how to implement YOLOv3 object detector from scratch. 


[code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/YOLOV3) | [blog](https://yunyang1994.gitee.io/2018/12/28/YOLOv3-算法的一点理解/)  | [issue](https://github.com/YunYang1994/tensorflow-yolov3/issues/39)



## part 1. Quick start

1. Clone this file

```bashrc

$ git clone https://github.com/YunYang1994/tensorflow-yolov3.git

```

2.  You are supposed  to install some dependencies before getting out hands with these codes.

```bashrc

$ cd tensorflow-yolov3

$ pip install -r ./docs/requirements.txt

```

3. Exporting loaded COCO weights as TF checkpoint(`yolov3_coco.ckpt`)【[BaiduCloud](https://pan.baidu.com/s/11mwiUy8KotjUVQXqkGGPFQ&shfl=sharepset)】

```bashrc

$ cd checkpoint

$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz

$ tar -xvf yolov3_coco.tar.gz

$ cd ..

$ python convert_weight.py

$ python freeze_graph.py

```

4. Then you will get some `.pb` files in the root path.,  and run the demo script

```bashrc

$ python image_demo.py

$ python video_demo.py # if use camera, set video_path = 0

```



    

    




## part 2. Train your own dataset

Two files are required as follows:



- [`dataset.txt`](https://raw.githubusercontent.com/YunYang1994/tensorflow-yolov3/master/data/dataset/voc_train.txt): 



```

xxx/xxx.jpg 18.19,6.32,424.13,421.83,20 323.86,2.65,640.0,421.94,20 

xxx/xxx.jpg 48,240,195,371,11 8,12,352,498,14

# image_path x_min, y_min, x_max, y_max, class_id  x_min, y_min ,..., class_id 

# make sure that x_max < width and y_max < height

```



- [`class.names`](https://github.com/YunYang1994/tensorflow-yolov3/blob/master/data/classes/coco.names):



```

person

bicycle

car

...

toothbrush

```



### 2.1 Train on VOC dataset

Download VOC PASCAL trainval  and test data

```bashrc

$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar

$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

```

Extract all of these tars into one directory and rename them, which should have the following basic structure.



```bashrc



VOC           # path:  /home/yang/dataset/VOC

├── test

|    └──VOCdevkit

|        └──VOC2007 (from VOCtest_06-Nov-2007.tar)

└── train

     └──VOCdevkit

         └──VOC2007 (from VOCtrainval_06-Nov-2007.tar)

         └──VOC2012 (from VOCtrainval_11-May-2012.tar)

                     

$ python scripts/voc_annotation.py --data_path /home/yang/test/VOC

```

Then edit your `./core/config.py` to make some necessary configurations



```bashrc

__C.YOLO.CLASSES                = "./data/classes/voc.names"

__C.TRAIN.ANNOT_PATH            = "./data/dataset/voc_train.txt"

__C.TEST.ANNOT_PATH             = "./data/dataset/voc_test.txt"

```

Here are two kinds of training method: 



##### (1) train from scratch:



```bashrc

$ python train.py

$ tensorboard --logdir ./data

```

##### (2) train from COCO weights(recommend):



```bashrc

$ cd checkpoint

$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz

$ tar -xvf yolov3_coco.tar.gz

$ cd ..

$ python convert_weight.py --train_from_coco

$ python train.py

```

### 2.2 Evaluate on VOC dataset



```

$ python evaluate.py

$ cd mAP

$ python main.py -na

```



the mAP on the VOC2012 dataset:





    

    




## part 3. Other Implementations



[-**`YOLOv3目标检测有了TensorFlow实现，可用自己的数据来训练`**](https://mp.weixin.qq.com/s/cq7g1-4oFTftLbmKcpi_aQ)



[-**`Stronger-yolo`**](https://github.com/Stinky-Tofu/Stronger-yolo)



[- **`Implementing YOLO v3 in Tensorflow (TF-Slim)`**](https://itnext.io/implementing-yolo-v3-in-tensorflow-tf-slim-c3c55ff59dbe)



[- **`YOLOv3_TensorFlow`**](https://github.com/wizyoung/YOLOv3_TensorFlow)



[- **`Object Detection using YOLOv2 on Pascal VOC2012`**](https://fairyonice.github.io/Part_1_Object_Detection_with_Yolo_for_VOC_2014_data_anchor_box_clustering.html)



[-**`Understanding YOLO`**](https://hackernoon.com/understanding-yolo-f5a74bbc7967)