README.txt

           Bilinear CNN (B-CNN) for Fine-grained recognition

DESCRIPTIONS After getting the deep descriptors of an image, bilinear pooling computes the sum of the outer product of those deep descriptors. Bilinear pooling captures all pairwise descriptor interactions, i.e., interactions of different part, in a translational invariant manner.

B-CNN provides richer representations than linear models, and B-CNN achieves
better performance than part-based fine-grained models with no need for
further part annotation.

Please note that this repo is relative old, which is writen in PyTorch 0.3.0.
If you are using newer version of PyTorch (say, >=0.4.0), it is suggested to
consider using this repo https://github.com/HaoMood/blinear-cnn-faster instead.

REFERENCE T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear CNN models for fine-grained visual recognition. In Proceedings of the IEEE International Conference on Computer Vision, pages 1449--1457, 2015.

PREREQUIREMENTS Python3.6 with Numpy supported PyTorch

LAYOUT ./data/ # Datasets ./doc/ # Automatically generated documents ./src/ # Source code

USAGE Step 1. Fine-tune the fc layer only. It gives 76.77% test set accuracy. $ CUDA_VISIBLE_DEVICES=0,1,2,3 ./src/bilinear_cnn_fc.py --base_lr 1.0
--batch_size 64 --epochs 55 --weight_decay 1e-8
| tee "[fc-] base_lr_1.0-weight_decay_1e-8-epoch_.log"

Step 2. Fine-tune all layers. It gives 84.17% test set accuracy.
$ CUDA_VISIBLE_DEVICES=0,1,2,3 ./src/bilinear_cnn_all.py --base_lr 1e-2 \
      --batch_size 64 --epochs 25 --weight_decay 1e-5 \
      --model "model.pth" \
      | tee "[all-] base_lr_1e-2-weight_decay_1e-5-epoch_.log"

AUTHOR Hao Zhang: zhangh0214@gmail.com

LICENSE CC BY-SA 3.0