chainercv:ChainerCV:计算机视觉深度学习库

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  • 2022-04-09 03:22
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ChainerCV:计算机视觉深度学习库 ChainerCV是一个工具,培训和运行基于计算机视觉的任务神经网络的集合 。 您可以在找到文档。 支持的任务: 图像分类( , , ) 目标检测(, , , , , ) 语义分割( , , ) 实例分割( , ) 指导原则 ChainerCV是根据以下三个指导原则开发的。 易用性-具有凝聚力和简单接口的计算机视觉网络的实现。 重现性-培训脚本非常适合用作参考实现。 组合性-具有通用API的工具,例如数据加载器和评估脚本。 安装 $ pip install -U numpy $ pip install chain
chainercv-master.zip
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# Classification ## ImageNet ### Weight conversion Single crop error rates of the models with the weights converted from Caffe weights. | Model | Top 1 | Original Top 1 | |:-:|:-:|:-:| | MobileNetV2-1.0 | 28.3 % | 28.0 % [6] | | MobileNetV2-1.4 | 24.3 % | 25.3 % [6] | | VGG16 | 29.0 % | 28.5 % [1] | | ResNet50 (`arch=he`) | 24.8 % | 24.7 % [2] | | ResNet101 (`arch=he`) | 23.6 % | 23.6 % [2] | | ResNet152 (`arch=he`) | 23.2 % | 23.0 % [2] | | SE-ResNet50 | 22.7 % | 22.4 % [3,4] | | SE-ResNet101 | 21.8 % | 21.8 % [3,4] | | SE-ResNet152 | 21.4 % | 21.3 % [3,4] | | SE-ResNeXt50 | 20.9 % | 21.0 % [3,4] | | SE-ResNeXt101 | 19.7 % | 19.8 % [3,4] | Ten crop error rate. | Model | Top 1 | Original Top 1 | |:-:|:-:|:-:| | MobileNetV2-1.0 | 25.6 % | | | MobileNetV2-1.4 | 22.4 % | | | VGG16 | 27.1 % | | | ResNet50 (`arch=he`) | 23.0 % | 22.9 % [2] | | ResNet101 (`arch=he`) | 21.8 % | 21.8 % [2] | | ResNet152 (`arch=he`) | 21.4 % | 21.4 % [2] | | SE-ResNet50 | 20.8 % | | | SE-ResNet101 | 20.1 % | | | SE-ResNet152 | 19.7 % | | | SE-ResNeXt50 | 19.4 % | | | SE-ResNeXt101 | 18.6 % | | The results can be reproduced by the following command. These scores are obtained using OpenCV backend. If Pillow is used, scores would differ. ``` $ python eval_imagenet.py <path_to_val_dataset> [--model mobilenet_v2|vgg16|resnet50|resnet101|resnet152|se-resnet50|se-resnet101|se-resnet152] [--pretrained-model <model_path>] [--batchsize <batchsize>] [--gpu <gpu>] [--crop center|10] ``` ### Trained model Single crop error rates of the models trained with the ChainerCV's training script. | Model | Top 1 | Original Top 1 | |:-:|:-:|:-:| | ResNet50 (`arch=fb`) | 23.51 % | 23.60% [5] | | ResNet101 (`arch=fb`) | 22.07 % | 22.08% [5] | | ResNet152 (`arch=fb`) | 21.67 % | | The scores of the models trained with `train_imagenet_multi.py`, which can be executed like below. Please consult the full list of arguments for the training script with `python train_imagenet_multi.py -h`. ``` $ mpiexec -n N python train_imagenet_multi.py <path_to_train_dataset> <path_to_val_dataset> ``` The training procedure carefully follows the "ResNet in 1 hour" paper [5]. #### Performance tip cuDNN convolution functions can be optimized with extra commands (see https://docs.chainer.org/en/stable/performance.html#optimize-cudnn-convolution). #### Detailed training results Here, we investigate the effect of the number of GPUs on the final performance. For more statistically reliable results, we obtained results from five different random seeds. | Model | # GPUs | Top 1 | |:-:|:-:|:-:| | ResNet50 (`arch=fb`) | 8 | 23.53 (std=0.06) | | ResNet50 (`arch=fb`) | 32 | 23.56 (std=0.11) | ## How to prepare ImageNet dataset This instructions are based on the instruction found [here](https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset). The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset has 1000 categories and 1.2 million images. The images do not need to be preprocessed or packaged in any database, but the validation images need to be moved into appropriate subfolders. 1. Download the images from http://image-net.org/download-images 2. Extract the training data: ```bash $ mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train $ tar -xvf ILSVRC2012_img_train.tar && mv ILSVRC2012_img_train.tar .. $ find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done $ cd .. ``` 3. Extract the validation data and move images to subfolders: ```bash $ mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar $ wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash $ mv ILSVRC2012_img_val.tar .. && cd .. ``` ## References 1. Karen Simonyan, Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition" ICLR 2015 2. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition" CVPR 2016 3. Jie Hu, Li Shen, Gang Sun. "Squeeze-and-Excitation Networks" CVPR 2018 4. https://github.com/hujie-frank/SENet 5. Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He. "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour" https://arxiv.org/abs/1706.02677 6. Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. "MobileNetV2: Inverted Residuals and Linear Bottlenecks" https://arxiv.org/abs/1801.04381
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