AI News, karpathy/neuraltalk2

karpathy/neuraltalk2

Update (September 22, 2016): The Google Brain team has released the image captioning model of Vinyals et al.

The core model is very similar to NeuralTalk2 (a CNN followed by RNN), but the Google release should work significantly better as a result of better CNN, some tricks, and more careful engineering.

This is an early code release that works great but is slightly hastily released and probably requires some code reading of inline comments (which I tried to be quite good with in general).

sudo apt-get install libprotobuf-dev protobuf-compiler), and then install via luarocks: Finally, you will also need to install torch-hdf5, and h5py, since we will be using hdf5 files to store the preprocessed data.

Quite a few dependencies, sorry no easy way around it :\ In this case you want to run the evaluation script on a pretrained model checkpoint. I

The eval script will create an vis.json file inside the vis folder, which can then be visualized with the provided HTML interface: Now visit localhost:8000 in your browser and you should see your predicted captions.

In this case simply leave out the image_folder option and the eval script and instead pass in the input_h5, input_json to your preprocessed files.

The notebook will combine the train/val data together and create a very simple and small json file that contains a large list of image paths, and raw captions for each image, of the form: Once we have this, we're ready to invoke the prepro.py script, which will read all of this in and create a dataset (an hdf5 file and a json file) ready for consumption in the Lua code.

For example, for MS COCO we can run the prepro file as follows: This is telling the script to read in all the data (the images and the captions), allocate 5000 images for val/test splits respectively, and map all words that occur <= 5 times to a special UNK token.

The train script will take over, and start dumping checkpoints into the folder specified by checkpoint_path (default = current folder).

If you'd like to evaluate BLEU/METEOR/CIDEr scores during training in addition to validation cross entropy loss, use -language_eval 1 option, but don't forget to download the coco-caption code into coco-caption directory.

1 epoch of training (with no finetuning - notice this is the default) takes about 1 hour and results in validation loss ~2.7 and CIDEr score of ~0.4.

I like to do the training in stages, where I first train with no finetuning, and then restart the train script with -finetune_cnn_after 0 to start finetuning right away, and using -start_from flag to continue from the previous model checkpoint.

No problem, create a json file in the exact same form as before, describing your JPG files: and invoke the prepro.py script to preprocess all the images and data into and hdf5 file and json file.

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