# tensorflow-yolo

**Repository Path**: xielinjiangs/tensorflow-yolo

## Basic Information

- **Project Name**: tensorflow-yolo
- **Description**: tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: python2.7
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-05
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# tensorflow-yolo

### Require
	tensorflow-1.0
### download pretrained model

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

```
	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
```