https://github.com/nilboy/tensorflow-yolo

tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)
https://github.com/nilboy/tensorflow-yolo

tensorflow tensorflow-yolo yolo

Last synced: about 2 months ago
JSON representation

tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)

• Host: GitHub
• URL: https://github.com/nilboy/tensorflow-yolo
• Owner: nilboy
• Created: 2016-10-12T13:48:04.000Z (about 8 years ago)
• Default Branch: python2.7
• Last Pushed: 2018-10-22T09:16:43.000Z (about 6 years ago)
• Last Synced: 2024-08-02T16:03:39.375Z (5 months ago)
• Topics: tensorflow, tensorflow-yolo, yolo
• Language: Python
• Homepage:
• Size: 107 KB
• Stars: 773
• Watchers: 30
• Forks: 315
• Open Issues: 45
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-yolo-object-detection - nilboy/tensorflow-yolo - yolo?style=social"/> : tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test). (Other Versions of YOLO)
• awesome-yolo-object-detection - nilboy/tensorflow-yolo - yolo?style=social"/> : tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test). (Other Versions of YOLO)

README

        
# tensorflow-yolo

### Require

	tensorflow-1.0

### download pretrained model

yolo_tiny: https://drive.google.com/file/d/0B-yiAeTLLamRekxqVE01Yi1RRlk/view?usp=sharing

```

	mv yolo_tiny.ckpt models/pretrain/ 

```

### Train

#### Train on pascal-voc2007 data 

##### Download pascal-Voc2007 data

1. Download the training, validation and test data

	```

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

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

	```

2. Extract all of these tars into one directory named `VOCdevkit`

	```

	tar xvf VOCtrainval_06-Nov-2007.tar

	tar xvf VOCtest_06-Nov-2007.tar

	```

3. It should have this basic structure

	```

  	$VOCdevkit/                           # development kit

  	$VOCdevkit/VOCcode/                   # VOC utility code

  	$VOCdevkit/VOC2007                    # image sets, annotations, etc.

  	# ... and several other directories ...

  	```

4. Create symlinks for the PASCAL VOC dataset

	```

    cd $YOLO_ROOT/data

    ln -s $VOCdevkit VOCdevkit2007

    ```

    Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.

#### convert the Pascal-voc data to text_record file

```

python tools/preprocess_pascal_voc.py

```

#### train

```

python tools/train.py -c conf/train.cfg

```

#### Train your customer data

1. transform your training data to text_record file(the format reference to pascal_voc)

2. write your own train-configure file

3. train (python tools/train.py -c $your_configure_file)

### test demo

```

python demo.py

```