{"id":21023813,"url":"https://github.com/shicai/densenet-caffe","last_synced_at":"2026-02-13T05:45:51.216Z","repository":{"id":145001177,"uuid":"81929000","full_name":"shicai/DenseNet-Caffe","owner":"shicai","description":"DenseNet Caffe Models, converted from 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networks, please read the original paper:\n- [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)\n\n\n### Pretrained DenseNet Models on ImageNet\n\nThe top-1/5 accuracy rates by using single center crop (crop size: 224x224, image size: 256xN)\n\nNetwork|Top-1|Top-5|Download|Architecture\n:---:|:---:|:---:|:---:|:---:\nDenseNet 121 (k=32)| 74.91| 92.19| [caffemodel (30.8  MB)](https://drive.google.com/open?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE)| [netscope](http://ethereon.github.io/netscope/#/gist/4928834eca7f06261ba0558e0ff63a6a), [netron](http://lutzroeder.github.io/netron?gist=4928834eca7f06261ba0558e0ff63a6a)\nDenseNet 169 (k=32)| 76.09| 93.14| [caffemodel (54.6  MB)](https://drive.google.com/open?id=0B7ubpZO7HnlCRWVVdUJjVVAyQXc)| [netscope](http://ethereon.github.io/netscope/#/gist/71335b6e8634327c9b9216619572b3dd), [netron](http://lutzroeder.github.io/netron?gist=71335b6e8634327c9b9216619572b3dd)\nDenseNet 201 (k=32)| 77.31| 93.64| [caffemodel (77.3  MB)](https://drive.google.com/open?id=0B7ubpZO7HnlCV3pud2oyR3lNMWs)| [netscope](http://ethereon.github.io/netscope/#/gist/ee808e19615844b8dbc7b13e92abd233), [netron](http://lutzroeder.github.io/netron?gist=ee808e19615844b8dbc7b13e92abd233)\nDenseNet 161 (k=48)| 77.64| 93.79| [caffemodel (110 MB)](https://drive.google.com/open?id=0B7ubpZO7HnlCa0phRGJIRERoTXM)| [netscope](http://ethereon.github.io/netscope/#/gist/8fae97d9c66b40b8da443f7f23e9b29b), [netron](http://lutzroeder.github.io/netron?gist=8fae97d9c66b40b8da443f7f23e9b29b)\n\n**Update** (July 27, 2017): for your convenience, we also provide a link to these models on [Baidu Disk](https://pan.baidu.com/s/1gfjD8cF).\n\n### Notes\nDue to compatibility reasons, several modifications have been made:\n- BGR mean values **[103.94,116.78,123.68]** are subtracted\n- **scale: 0.017** is used, instead of the original std values for image preprocessing\n- **ceil_mode: false** is used in the first pooling layers ('pool1') 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