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.

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 https://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.

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.

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!

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