# Basic_CNNs_TensorFlow2
**Repository Path**: xxuffei/Basic_CNNs_TensorFlow2
## Basic Information
- **Project Name**: Basic_CNNs_TensorFlow2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-17
- **Last Updated**: 2021-03-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Basic_CNNs_TensorFlow2
A tensorflow2 implementation of some basic CNNs.
## Networks included:
+ MobileNet_V1
+ MobileNet_V2
+ [MobileNet_V3](https://github.com/calmisential/MobileNetV3_TensorFlow2)
+ [EfficientNet](https://github.com/calmisential/EfficientNet_TensorFlow2)
+ [ResNeXt](https://github.com/calmisential/ResNeXt_TensorFlow2)
+ [InceptionV4, InceptionResNetV1, InceptionResNetV2](https://github.com/calmisential/InceptionV4_TensorFlow2)
+ SE_ResNet_50, SE_ResNet_101, SE_ResNet_152, SE_ResNeXt_50, SE_ResNeXt_101
+ SqueezeNet
+ [DenseNet](https://github.com/calmisential/DenseNet_TensorFlow2)
+ ShuffleNetV2
+ [ResNet](https://github.com/calmisential/TensorFlow2.0_ResNet)
## Other networks
For AlexNet and VGG, see : https://github.com/calmisential/TensorFlow2.0_Image_Classification
For InceptionV3, see : https://github.com/calmisential/TensorFlow2.0_InceptionV3
For ResNet, see : https://github.com/calmisential/TensorFlow2.0_ResNet
## Train
1. Requirements:
+ Python >= 3.6
+ Tensorflow >= 2.3.0rc1
2. To train the network on your own dataset, you can put the dataset under the folder **original dataset**, and the directory should look like this:
```
|——original dataset
|——class_name_0
|——class_name_1
|——class_name_2
|——class_name_3
```
3. Run the script **split_dataset.py** to split the raw dataset into train set, valid set and test set. The dataset directory will be like this:
```
|——dataset
|——train
|——class_name_1
|——class_name_2
......
|——class_name_n
|——valid
|——class_name_1
|——class_name_2
......
|——class_name_n
|—-test
|——class_name_1
|——class_name_2
......
|——class_name_n
```
4. Run **to_tfrecord.py** to generate tfrecord files.
5. Change the corresponding parameters in **config.py**.
6. Run **train.py** to start training.
If you want to train the *EfficientNet*, you should change the IMAGE_HEIGHT and IMAGE_WIDTH to *resolution* in the params, and then run **train.py** to start training.
## Evaluate
Run **evaluate.py** to evaluate the model's performance on the test dataset.
## Different input image sizes for different neural networks
| Type |
Neural Network |
Input Image Size (height * width) |
| MobileNet |
MobileNet_V1 |
(224 * 224) |
| MobileNet_V2 |
(224 * 224) |
| MobileNet_V3 |
(224 * 224) |
| EfficientNet |
EfficientNet(B0~B7) |
/ |
| ResNeXt |
ResNeXt50 |
(224 * 224) |
| ResNeXt101 |
(224 * 224) |
| SEResNeXt |
SEResNeXt50 |
(224 * 224) |
| SEResNeXt101 |
(224 * 224) |
| Inception |
InceptionV4 |
(299 * 299) |
| Inception_ResNet_V1 |
(299 * 299) |
| Inception_ResNet_V2 |
(299 * 299) |
| SE_ResNet |
SE_ResNet_50 |
(224 * 224) |
| SE_ResNet_101 |
(224 * 224) |
| SE_ResNet_152 |
(224 * 224) |
SqueezeNet |
SqueezeNet |
(224 * 224) |
| DenseNet |
DenseNet_121 |
(224 * 224) |
| DenseNet_169 |
(224 * 224) |
| DenseNet_201 |
(224 * 224) |
| DenseNet_269 |
(224 * 224) |
| ShuffleNetV2 |
ShuffleNetV2 |
(224 * 224) |
| ResNet |
ResNet_18 |
(224 * 224) |
| ResNet_34 |
(224 * 224) |
| ResNet_50 |
(224 * 224) |
| ResNet_101 |
(224 * 224) |
| ResNet_152 |
(224 * 224) |
## References
1. MobileNet_V1: [Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
2. MobileNet_V2: [Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
3. MobileNet_V3: [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)
4. EfficientNet: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
5. The official code of EfficientNet: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
6. ResNeXt: [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431)
7. Inception_V4/Inception_ResNet_V1/Inception_ResNet_V2: [Inception-v4, Inception-ResNet and the Impact of Residual Connectionson Learning](https://arxiv.org/abs/1602.07261)
8. The official implementation of Inception_V4: https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py
9. The official implementation of Inception_ResNet_V2: https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
10. SENet: [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
11. SqueezeNet: [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360)
12. DenseNet: [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
13. https://zhuanlan.zhihu.com/p/37189203
14. ShuffleNetV2: [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
](https://arxiv.org/abs/1807.11164)
15. https://zhuanlan.zhihu.com/p/48261931
16. ResNet: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)