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src=\"https://tiyaro-public-docs.s3.us-west-2.amazonaws.com/assets/tiyaro_badge.svg\"\u003e\u003c/a\u003e\r\n\r\n\r\n## Overview\r\n\r\nThis repository contains an op-for-op PyTorch reimplementation of [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).\r\n\r\n## Table of contents\r\n\r\n- [AlexNet-PyTorch](#alexnet-pytorch)\r\n    - [Overview](#overview)\r\n    - [Table of contents](#table-of-contents)\r\n    - [Download weights](#download-weights)\r\n    - [Download datasets](#download-datasets)\r\n    - [How Test and Train](#how-test-and-train)\r\n        - [Test](#test)\r\n        - [Train model](#train-model)\r\n        - [Resume train model](#resume-train-model)\r\n    - [Result](#result)\r\n    - [Contributing](#contributing)\r\n    - [Credit](#credit)\r\n        - [ImageNet Classification with Deep Convolutional Neural Networks](#imagenet-classification-with-deep-convolutional-neural-networks)\r\n\r\n## Download weights\r\n\r\n- [Google Driver](https://drive.google.com/drive/folders/17ju2HN7Y6pyPK2CC_AqnAfTOe9_3hCQ8?usp=sharing)\r\n- [Baidu Driver](https://pan.baidu.com/s/1yNs4rqIb004-NKEdKBJtYg?pwd=llot)\r\n\r\n## Download datasets\r\n\r\nContains MNIST, CIFAR10\u0026CIFAR100, TinyImageNet_200, MiniImageNet_1K, ImageNet_1K, Caltech101\u0026Caltech256 and more etc.\r\n\r\n- [Google Driver](https://drive.google.com/drive/folders/1f-NSpZc07Qlzhgi6EbBEI1wTkN1MxPbQ?usp=sharing)\r\n- [Baidu Driver](https://pan.baidu.com/s/1arNM38vhDT7p4jKeD4sqwA?pwd=llot)\r\n\r\nPlease refer to `README.md` in the `data` directory for the method of making a dataset.\r\n\r\n## How Test and Train\r\n\r\nBoth training and testing only need to modify the `config.py` file. \r\n\r\n### Test\r\n\r\n- line 29: `model_num_classes` change to `1000`.\r\n- line 31: `mode` change to `test`.\r\n- line 79: `model_path` change to `./results/pretrained_models/AlexNet-ImageNet_1K-9df8cd0f.pth.tar`.\r\n\r\n```bash\r\npython3 test.py\r\n```\r\n\r\n### Train model\r\n\r\n- line 29: `model_num_classes` change to `1000`.\r\n- line 31: `mode` change to `train`.\r\n- line 33: `exp_name` change to `AlexNet-ImageNet_1K`.\r\n- line 45: `pretrained_model_path` change to `./results/pretrained_models/AlexNet-ImageNet_1K-9df8cd0f.pth.tar`.\r\n\r\n```bash\r\npython3 train.py\r\n```\r\n\r\n### Resume train model\r\n\r\n- line 29: `model_num_classes` change to `1000`.\r\n- line 31: `mode` change to `train`.\r\n- line 33: `exp_name` change to `AlexNet-ImageNet_1K`.\r\n- line 48: `resume` change to `./samples/AlexNet-ImageNet_1K/epoch_xxx.pth.tar`.\r\n\r\n```bash\r\npython3 train.py\r\n```\r\n\r\n## Result\r\n\r\nSource of original paper results: [https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf](https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)\r\n\r\nIn the following table, the top-x error value in `()` indicates the result of the project, and `-` indicates no test.\r\n\r\n|  Model  |   Dataset   | Top-1 error (val) | Top-5 error (val) |\r\n|:-------:|:-----------:|:-----------------:|:-----------------:|\r\n| AlexNet | ImageNet_1K | 36.7%(**43.8%**)  | 15.4%(**21.3%**)  |\r\n\r\n```bash\r\n# Download `AlexNet-ImageNet_1K-9df8cd0f.pth.tar` weights to `./results/pretrained_models`\r\n# More detail see `README.md\u003cDownload weights\u003e`\r\npython3 ./inference.py \r\n```\r\n\r\nInput: \r\n\r\n\u003cspan align=\"center\"\u003e\u003cimg width=\"224\" height=\"224\" src=\"figure/n01440764_36.JPEG\"/\u003e\u003c/span\u003e\r\n\r\nOutput: \r\n\r\n```text\r\nBuild AlexNet model successfully.\r\nLoad AlexNet model weights `/AlexNet-PyTorch/results/pretrained_models/AlexNet-ImageNet_1K-9df8cd0f.pth.tar` successfully.\r\ntench, Tinca tinca                                                          (95.73%)\r\nbolete                                                                      (1.20%)\r\ntriceratops                                                                 (0.43%)\r\nplatypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus (0.36%)\r\ncroquet ball                                                                (0.28%)\r\n```\r\n\r\n## Contributing\r\n\r\nIf you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.\r\n\r\nI look forward to seeing what the community does with these models!\r\n\r\n### Credit\r\n\r\n#### ImageNet Classification with Deep Convolutional Neural Networks\r\n\r\n*Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton*\r\n\r\n##### Abstract\r\n\r\nWe trained a large, deep convolutional neural network to classify the 1.2 million\r\nhigh-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%\r\nand 17.0% which is considerably better than the previous state-of-the-art. The\r\nneural network, which has 60 million parameters and 650,000 neurons, consists\r\nof five convolutional layers, some of which are followed by max-pooling layers,\r\nand three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected\r\nlayers we employed a recently-developed regularization method called “dropout”\r\nthat proved to be very effective. We also entered a variant of this model in the\r\nILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,\r\ncompared to 26.2% achieved by the second-best entry.\r\n\r\n[[Paper]](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\r\n\r\n```bibtex\r\n@article{AlexNet,\r\n    title = {ImageNet Classification with Deep Convolutional Neural Networks},\r\n    author = {Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton},\r\n    journal = {nips},\r\n    year = {2012}\r\n}\r\n```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flornatang%2Falexnet-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flornatang%2Falexnet-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flornatang%2Falexnet-pytorch/lists"}