Right Whale Recognition
These steps take about 6 hours on a system with 8 processors and an NVIDIA Titan X GPU. Tested only on Ubuntu.
Download and install neon 1.1.4
git clone https://github.com/NervanaSystems/neon.git cd neon git checkout e09fc11 make source .venv/bin/activate
pip install scipy scikit-image
Download the following files from Kaggle:
imgs.zip train.csv w_7489.jpg sample_submission.csv
Save these to a directory that we will refer to as /path/to/data.
Clone this repository
git clone https://github.com/anlthms/whale-2015.git cd whale-2015
Train models and generate predictions
Submit subm.csv.gz to Kaggle
- To run on a system that does not have a GPU:
./run.sh /path/to/data -bcpu
- For quicker results, decrease
- The script run.sh first prepares the data for training. If you want to repeat the preparation step, delete the file /path/to/data/prepdone before invoking run.sh again.
- If using a GPU, the results are non-deterministic regardless of how the random number generator is seeded.
- The localizer uses a heuristic to determine when to stop training. If a good optimum is not detected, a message that says "WARNING: model may not be optimal" is displayed.