# pretrained-models **Repository Path**: jlj52/pretrained-models ## Basic Information - **Project Name**: pretrained-models - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-06-08 - **Last Updated**: 2021-06-08 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Some Pretrained Models for TensorLayer Feel free to add more. ## Reinforcement Learning Examples `./rl_models/` contains pretrained models for each algorithm in [reinforcement learning examples](https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning). ## CNN for ImageNet The `tl.models` API description [here](http://tensorlayer.readthedocs.io/en/latest/modules/models.html), and the discussion for network architecture that can be easily use [here](https://github.com/tensorlayer/tensorlayer/issues/367). | Model | Code | Parameter | Top-1 Accuracy | Top-5 Accuracy | | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------- | -------------- | | [VGG 16](http://arxiv.org/abs/1409.1556.pdf) | [code](https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_vgg16.py) | [model](https://www.cs.toronto.edu/~frossard/vgg16/vgg16_weights.npz) | 71.5 | 89.8 | | [VGG 19](http://arxiv.org/abs/1409.1556.pdf) | [code](https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_vgg19.py) | [model](https://drive.google.com/file/d/1pZ0v-sLj-glfSx3Cssk_aBFRI8mF0hiq/view?usp=sharing) (from [machrisaa/tensorflow-vgg](https://github.com/machrisaa/tensorflow-vgg)) | 71.1 | 89.8 | | [ResNet V1 50](https://arxiv.org/abs/1512.03385) | | | 75.2 | 92.2 | | [ResNet V1 101](https://arxiv.org/abs/1512.03385) | | [resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) | 76.4 | 92.9 | | [ResNet V1 152](https://arxiv.org/abs/1512.03385) | | [resnet_v1_152_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz) | 76.8 | 93.2 | | [ResNet V2 50](https://arxiv.org/abs/1603.05027) | | [resnet_v2_50_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz) | 75.6 | 92.8 | | [ResNet V2 101](https://arxiv.org/abs/1603.05027) | | [resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz) | 77.0 | 93.7 | | [ResNet V2 152](https://arxiv.org/abs/1603.05027) | | [resnet_v2_152_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz) | 77.8 | 94.1 | | [ResNet V2 200](https://arxiv.org/abs/1603.05027) | | [TBA]() | 79.9\* | 95.2\* | | [Inception V1](http://arxiv.org/abs/1409.4842v1) | | [inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz) | 69.8 | 89.6 | | [Inception V2](http://arxiv.org/abs/1502.03167) | | [inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz) | 73.9 | 91.8 | | [Inception V3](http://arxiv.org/abs/1512.00567) | [code](https://github.com/tensorlayer/tensorlayer/blob/TensorLayer2.0/master/examples/pretrained_cnn/tutorial_inceptionV3_tfslim.py) | [inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz) | 78.0 | 93.9 | | [Inception V4](http://arxiv.org/abs/1602.07261) | | | 80.2 | 95.2 | | [Xception](http://openaccess.thecvf.com/content_cvpr_2017/papers/Chollet_Xception_Deep_Learning_CVPR_2017_paper.pdf) | | | | | | [Inception-ResNet-v2](http://arxiv.org/abs/1602.07261) | | | 80.4 | 95.3 | | [SqueezeNet V1](https://arxiv.org/abs/1602.07360) | [code](https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_squeezenetv1.py) | [model](https://github.com/tensorlayer/pretrained-models/blob/master/models/squeezenet.npz) | | | | [SqueezeNet V2](https://arxiv.org/abs/1602.07360) | | | | | | [MobileNet V1](https://arxiv.org/pdf/1704.04861.pdf) | [code](https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_mobilenetv1.py) | [model](https://github.com/tensorlayer/pretrained-models/blob/master/models/mobilenet.npz) | | | | [MobileNet V2_1.4_224](https://arxiv.org/abs/1801.04381) | | | 74.9 | 92.5 | | [MobileNet V2_1.0_224](https://arxiv.org/abs/1801.04381) | | | 71.9 | 91.0 | | [NASNet-A_Mobile_224](https://arxiv.org/abs/1707.07012) | | [nasnet-a_mobile_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz) | 74.0 | 91.6 | | [NASNet-A_Large_331](https://arxiv.org/abs/1707.07012) | | [nasnet-a_large_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz) | 82.7 | 96.2 | | [PNASNet-5_Large_331](https://arxiv.org/abs/1712.00559) | | [pnasnet-5_large_2017_12_13.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/pnasnet-5_large_2017_12_13.tar.gz) | 82.9 | 96.2 | | [DenseNet](https://arxiv.org/abs/1608.06993) | | | | | | NASNet | | | | | More examples in [Awesome-TensorLayer](https://github.com/tensorlayer/awesome-tensorlayer/edit/master/readme.md) ## References - [TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models) - [Keras](https://keras.io/applications/#applications)