Here we release our trained VGGNet models on the large-scale Places205 dataset, called Places205-VGGNet models, from the following report:
https://arxiv.org/abs/1508.01667
Places205-VGGNet Models for Scene Recognition
Limin Wang, Sheng Guo, Weilin Huang, and Yu Qiao, in arXive 1508.01667, 2015
| Model | top-1 val/test | top-5 val/test |
|---|---|---|
| Places205-VGGNet-11 | 58.6/59.0 | 87.6/87.6 |
| Places205-VGGNet-13 | 60.2/60.1 | 88.1/88.5 |
| Places205-VGGNet-16 | 60.6/60.3 | 88.5/88.8 |
| Places205-VGGNet-19 | 61.3/61.2 | 88.8/89.3 |
We use 5 crops and their horizontal flippings of each image for testing.
| Model | MIT67 | SUN397 |
|---|---|---|
| Places205-VGGNet-11 | 82.0 | 65.3 |
| Places205-VGGNet-13 | 81.9 | 66.7 |
| Places205-VGGNet-16 | 81.2 | 66.9 |
We extract the fc6-layer features of our trained Places205-VGGNet models, which are further normalized by L2-norm.
- Places205-VGGNet-11:
https://mmlab.siat.ac.cn/Places205-VGGNet/siat_scene_vgg_11.caffemodel - Places205-VGGNet-13:
https://mmlab.siat.ac.cn/Places205-VGGNet/siat_scene_vgg_13.caffemodel - Places205-VGGNet-16:
https://mmlab.siat.ac.cn/Places205-VGGNet/siat_scene_vgg_16.caffemodel - Places205-VGGNet-19:
https://mmlab.siat.ac.cn/Places205-VGGNet/siat_scene_vgg_19.caffemodel - Mean file:
https://mmlab.siat.ac.cn/Places205-VGGNet/places205_mean.mat
These models are relased for non-conmercial use. If you use these models in your research, thanks to cite our above report.
In order to speed up the training procedure of VGGNets, we use a Multi-GPU extension of Caffe toolbox:
https://github.com/yjxiong/caffe/tree/action_recog
Meanwhile, we add the strategies of multi-scale cropping and corner cropping provided by this extension, which has been proved to be effective for action recognition in videos.
Contact
- Limin Wang
- [Sheng Guo] (mailto:[email protected])
- Weilin Huang