Mean field approximation of Bilinear 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.
This project aims at accelerating training at the first step. We extract
VGG-16 relu5-3 features from ImageNet pre-trained model in advance and save
them onto disk. At the first step, we train the model directly from the
extracted relu5-3 features. We avoid feed forwarding convolution layers
multiple times.
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.
# Get relu5-3 features from VGG-16 ImageNet pre-trained model.
# It gives 75.47% accuracy on CUB.
$ CUDA_VISIBLE_DEVICES=0 ./src/get_conv.py
$ CUDA_VISIBLE_DEVICES=0,1,2,3 ./src/train.py --base_lr 1e0 \
--batch_size 64 --epochs 80 --weight_decay 1e-5 \
| tee "[fc-] base_lr_1e0-weight_decay_1e-5_.log"
Step 2. Fine-tune all layers.
# It gives 84.41% accuracy on CUB.
$ CUDA_VISIBLE_DEVICES=0,1,2,3 ./src/train.py --base_lr 1e-2 \
--batch_size 64 --epochs 80 --weight_decay 1e-5 \
--pretrained "bcnn_fc_epoch_.pth" \
| tee "[all-] base_lr_1e-2-weight_decay_1e-5.log"
AUTHOR
Hao Zhang: zhangh0214@gmail.com
LICENSE
CC BY-SA 3.0