Torch implementation of the Fast R-CNN

This is a torch implementation of the Fast R-CNN proposed by Girshick et .al. [1].

Requirements

• You need to install torch.
• The current implementation uses these torch packages:

  nn, inn, cudnn, image, matio, optim, paths

• You need a machine with CUDA GPU.
• You need to download the required weights and proposal files as discussed below.

Running the demo

For running the demo you only need to download the weights of the Fast R-CNN network. Please run

./scripts/get_frcnn_models.sh

lua
qlua demo.lua

Training the Fast-RCNN network

For training the Fast R-CNN network, you should download the pre-trained models, object proposals, and the Pascal VOC dataset. You can download the pre-trained AlexNet model and object proposals for VOC by running

./scripts/get_imgnet_models.sh

./scripts/get_proposals.sh

Now you can train the model by calling

th main_train.lua

th main_train.lua -help

• At least you need to specify the path to the parent directory of the VOC devkit folder by setting

  -dataset_path 'path to the directory'

  (the default is

  ./data/datasets/

  ) and the name of the VOC devkit folder by setting

  -dataset 'name of the root folder'

  (the default is

  voc_2007

  ).
• You can change the training set from trainval to train by passing

  -train_img_set 'train'

  .
• If you want to train a model other than AlexNet, you need to add a Lua definition of the model in the

  ./models

  folder and pass its location by setting the

  -model_def 'path to the model definition'

  option. In that case, you probably need to specify the appropriate pre-trained model for fine-tuning by passing the pre-trained path by setting the

  -pre_trained_file 'path to the pre-trained file'

  .
• The models together with a text file describing the configuration of the training will be saved into the

  ./data/trained_models/

  . You can change this directory by passing the

  -save_path 'your new path to save the models'

  option.
• The loss values and the loss diagrams will be saved into

  ./data/log/

  . You can change it by passing

  -log_path 'new path for saving the log files'

  .

Testing the Fast-RCNN network

Currently, you need Matlab to use the VOC evaluation functions. Please download the VOC2007 test data if you want to test on the test set. You can test your trained model by running

th main_test.lua

-model_weights 'path to the file containing the weights of your trained model'

-model_def 'path to the model definition'

-dataset_path 'path to the directory'

-dataset 'name of the root folder'

-test_img_set 'your test set'