# TensorFlow2.0_Image_Classification

**Repository Path**: futureflsl/TensorFlow2.0_Image_Classification

## Basic Information

- **Project Name**: TensorFlow2.0_Image_Classification
- **Description**: A TensorFlow_2.0 implementation of AlexNet and VGGNet.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2020-07-09
- **Last Updated**: 2023-03-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TensorFlow2.0_Image_Classification(include AlexNet and VGGNet)
This project uses TensorFlow2.0 for image classification tasks.

## How to use
### Requirements
+ **Python 3.x** (My Python version is 3.6.8)<br/>
+ **TensorFlow version: 2.0.0-beta1**<br/> 
+ The file directory of the dataset should look like this: <br/>
```
${dataset_root}
|——train
|   |——class_name_0
|   |——class_name_1
|   |——class_name_2
|   |——class_name_3
|——valid
|   |——class_name_0
|   |——class_name_1
|   |——class_name_2
|   |——class_name_3
|——test
    |——class_name_0
    |——class_name_1
    |——class_name_2
    |——class_name_3
```

### Train
Run the script
```
python train.py
```
to train the network on your image dataset, the final model will be stored. You can also change the corresponding training parameters in the `config.py`.<br/>

### Evaluate
To evaluate the model's performance on the test dataset, you can run `evaluate.py`.<br/>

The structure of the network is defined in `model_definition.py`, you can change the network structure to whatever you like.<br/>

## References
1. AlexNet : http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
2. VGG : https://arxiv.org/abs/1409.1556