文件列表:

LICENSE (1069, 2020-07-11)
configuration.py (1507, 2020-07-11)
dataset (0, 2020-07-11)
evaluate.py (1488, 2020-07-11)
models (0, 2020-07-11)
models\__init__.py (0, 2020-07-11)
models\densenet.py (5997, 2020-07-11)
models\efficientnet.py (10392, 2020-07-11)
models\group_convolution.py (10526, 2020-07-11)
models\inception_modules.py (16691, 2020-07-11)
models\inception_resnet_v1.py (11136, 2020-07-11)
models\inception_resnet_v2.py (9326, 2020-07-11)
models\inception_v4.py (1837, 2020-07-11)
models\mobilenet_v1.py (5460, 2020-07-11)
models\mobilenet_v2.py (6050, 2020-07-11)
models\mobilenet_v3_block.py (2841, 2020-07-11)
models\mobilenet_v3_large.py (4287, 2020-07-11)
models\mobilenet_v3_small.py (3634, 2020-07-11)
models\residual_block.py (3885, 2020-07-11)
models\resnet.py (4266, 2020-07-11)
models\resnext.py (2649, 2020-07-11)
models\resnext_block.py (2481, 2020-07-11)
models\se_resnet.py (5056, 2020-07-11)
models\se_resnext.py (5759, 2020-07-11)
models\shufflenet_v2.py (6307, 2020-07-11)
models\squeezenet.py (3287, 2020-07-11)
original_dataset (0, 2020-07-11)
parse_tfrecord.py (481, 2020-07-11)
predict.py (890, 2020-07-11)
prepare_data.py (2451, 2020-07-11)
saved_model (0, 2020-07-11)
split_dataset.py (3761, 2020-07-11)
test_single_image.py (1048, 2020-07-11)
to_tfrecord.py (2438, 2020-07-11)
... ...

# 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/1807.111***) 15. https://zhuanlan.zhihu.com/p/48261931 16. ResNet: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)

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