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VOC 2007 / 2012 classification

This code trains and evaluates a 20-way binary classifier on the PASCAL VOC dataset. It uses the stardard sigmoid cross entropy loss and evaluates mAP.

Setup

Clone this repo and also get my caffe future branch https://github.com/philkr/caffe/tree/future (I use a few custom layers, mainly HDF5 related) and compile it.

Download the VOC 2007 dataset from http://host.robots.ox.ac.uk/pascal/VOC/voc2007/.

Setup your own user_config.py, see user_config.example.py for an example. You'll need to setup CAFFE_DIR to point to the caffe future branch, and VOC_DIR to point to the Pascal VOC directory.

Now everything should be ready.

Running the evaluation

The basic evaluation is run through

python3 src/train_cls.py path/to/your.prototxt path/to/your.caffemodel

If you want to automatically train all variations fc8 only, fc6-fc8 and conv1-fc8 for your model you can use the make_matrix.sh script. It relies on the wait_for_gpu script, which tries to wait for a free gpu before running a job (this is a very crude solution and you probably want to use something like slurm in production).

bash make_matrix.sh experiments/ALL_MODELS experiments/ALL_PARAMS output/directory

This script will run every model described in experiments/ALL_MODELS with every parameter setting described in experiments/ALL_PARAMS. You can then plot the results using the make_table.py script.

python3 make_table.py output/directory

NOTE: make_table.py currently doesn't to a perfect job in figuring out what parameters and models were used. If somebody want to fix this be my guest.

Overfitting and 'tuning' the classifier

I have found the default settings to work in all the evaluations and comparisons I have performed. I'm sure there are certain learning rate and weight decay settings that will work better for some models. I however do not have the time to find them. The current parameters are tuned such that alexnet trained on imagenet works ok mAP of 80%. If you want to tune the parameters for your own model, be my guest, but please do it also for all baseline models too!

Results

I'll publish a up to date list of the top performing methods here, as soon as I get a chance.