We provide a number of pre-trained models for easy use. When using these models, please cite and heed licenses accordingly! We also welcome community contributions via pull requests to the repository.
You can find a number of demos showing how to download and run the models below here.
This list contains links to architecture definitions (
*.json), download scripts for the mean images (
*.tensor), and pre-trained weights (
*.marvin). We have also taken care to give credit where it is due, so please let us know if there is anything wrong or missing.
|AlexNet for ImageNet||ImageNet1k||Download||BVLC||C1 C2 C3||L1 L2 L3|
|AlexNet for Places||Places205||Download||Places||C2 C4||L2 L4|
|GoogLeNet for ImageNet||ImageNet1k||Download||BVLC||C1 C3 C5||L1 L3 L5|
|GoogLeNet for Places||Places205||Download||Places||C4 C5||L4 L5|
|VGGNet 16 for ImageNet||ImageNet1k||Download||VGG||C3 C6||L3 L6|
|VGGNet 19 for ImageNet||ImageNet1k||Download||VGG||C3 C6||L3 L6|
- Jia, Yangqing, et al. "Caffe: Convolutional architecture for fast feature embedding." Proceedings of the ACM International Conference on Multimedia. 2014.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
- Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." IEEE Conference on Computer Vision and Pattern Recognition. 2009.
- Zhou, Bolei, et al. "Learning deep features for scene recognition using places database." Advances in Neural Information Processing Systems. 2014.
- Szegedy, Christian, et al. "Going deeper with convolutions." arXiv preprint arXiv:1409.4842. 2014.
- Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556. 2014. </ol>