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Pre-trained models

BVLC aims to provide a variety of high quality pre-trained models. Note that unlike Caffe itself, these models are licensed for academic research / non-commercial use only. If you have any questions, please get in touch with us.

This page will be updated as more models become available.

ImageNet

Caffe Reference ImageNet Model: Our reference implementation of an ImageNet model trained on ILSVRC-2012 can be downloaded (232.6MB) by running examples/imagenet/get_caffe_reference_imagenet_model.sh from the Caffe root directory.

  • The bundled model is the iteration 310,000 snapshot.
  • The best validation performance during training was iteration 313,000 with validation accuracy 57.412% and loss 1.82328.

AlexNet: Our training of the Krizhevsky architecture, which differs from the paper's methodology by (1) not training with the relighting data-augmentation and (2) initializing non-zero biases to 0.1 instead of 1. (2) was found necessary for training, as initialization to 1 gave flat loss. Download the model (243.9MB) by running examples/imagenet/get_caffe_alexnet_model.sh from the Caffe root directory.

  • The bundled model is the iteration 360,000 snapshot.
  • The best validation performance during training was iteration 358,000 with validation accuracy 57.258% and loss 1.83948.

R-CNN (ILSVRC13): The pure Caffe instantiation of the R-CNN model for ILSVRC13 detection. Download the model (230.8MB) by running examples/imagenet/get_caffe_rcnn_imagenet_model.sh from the Caffe root directory. This model was made by transplanting the R-CNN SVM classifiers into a fc-rcnn classification layer, provided here as an off-the-shelf Caffe detector. Try the detection example to see it in action. For the full details, refer to the R-CNN site. N.B. For research purposes, make use of the official R-CNN package and not this example.

Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run data/ilsvrc12/get_ilsvrc_aux.sh from the root directory to obtain it.