From 6433ec9e98b52f87b2ffcad8d17f2dc01ed38b8d Mon Sep 17 00:00:00 2001 From: KaiyangZhou Date: Mon, 30 Apr 2018 12:41:10 +0100 Subject: [PATCH] update readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c190118..7dd0e33 100755 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ We support - download of trained models. ## Updates -- Apr 2018: Added [DukeMTMC-reID](https://github.com/layumi/DukeMTMC-reID_evaluation#dukemtmc-reid-description); Added [SqueezeNet](https://arxiv.org/abs/1602.07360) and [MobileNetV2 (CVPR'18)](https://arxiv.org/abs/1801.04381). +- Apr 2018: Added [DukeMTMC-reID](https://github.com/layumi/DukeMTMC-reID_evaluation#dukemtmc-reid-description); Added [SqueezeNet](https://arxiv.org/abs/1602.07360), [MobileNetV2 (CVPR'18)](https://arxiv.org/abs/1801.04381) and [ShuffleNet (CVPR'18)](https://arxiv.org/abs/1707.01083). - Apr 2018: Added [Harmonious Attention CNN (CVPR'18)](https://arxiv.org/abs/1802.08122). We achieved Rank-1 42.4% (vs. 41.7% in the paper) on CUHK03 (Detected) by training from scratch. The result can be reproduced by `python train_img_model_xent.py -d cuhk03 -a hacnn --save-dir log/hacnn-xent-cuhk03 --height 160 --width 64 --max-epoch 500 --stepsize -1 --eval-step 50`. - Apr 2018: Code upgraded to pytorch 0.4.0. - Apr 2018: Added [CUHK03](http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html). Models are [available](https://github.com/KaiyangZhou/deep-person-reid#cuhk03-detected-new-protocol-767700). @@ -121,6 +121,7 @@ These two classes are used for [torch.utils.data.DataLoader](http://pytorch.org/ * `models/HACNN.py`: HACNN [15]. * `models/SqueezeNet.py`: SqueezeNet [18]. * `models/MobileNet.py`: MobileNetV2 [19]. +* `models/ShuffleNet.py`: ShuffleNet [20]. See `models/__init__.py` for details regarding how to call these models in command line. @@ -261,3 +262,4 @@ Of course, you can pass `model.classifier.parameters()` to optimizer if you only [17] [Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.](https://arxiv.org/abs/1701.07717)
[18] [Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv:1602.07360.](https://arxiv.org/abs/1602.07360)
[19] [Sandler et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018.](https://arxiv.org/abs/1801.04381)
+[20] [Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. CVPR 2018.](https://arxiv.org/abs/1707.01083)