Implementation of Recurrent Convolutional Neural Network for Object Recognition.

Refer to requirements.txt

Here is the architecture of the model.

Refer to the paper below for more details.

the implementation is a little bit different from the original paper.

• Instead of LRN, using BN here and removing dropout layers except the last linear layer.

• As BN layers introduce extra parameters, the feed-forward filter size in layer 1 is modified to 3 x 3.

• For simplification, using Tencorp (there exsits api in pytorch) for test here instead of the original nine crops.

usage: train.py [-h] [-n K] [-b BATCH_SIZE] [-e EPOCH] [-s SAVE_DIR] [-l TRAINING_LOG]

Train RCNN

optional arguments: -h, --help show this help message and exit -n K the parameter K for RCNN -b BATCH_SIZE the batch size in just one gpu, * GPU_COUNT -e EPOCH the training epoch -s SAVE_DIR the model parameters to be saved -l TRAINING_LOG the logs to be saved

With Data Augmentation

The result improves slightly compared with that in 4.2.2.Maybe the hyper-parameter can be adjusted to get a better model.