Project as part of Dropbox's Hack Week to provide a classifier for detecting clouds in remote sensing data using deep learning.
TODO(brad): Setup virtualenv for training and inference boundary box code. TODO(brad): Consolidate all the README files and clean up this file.
Preprocessed datasets are in data/landsat/images and data/planetlab/images; however, these are not checked in due to possible licensing issues. Metadata that we've added via annotation in order to label the data is checked in and is in data/landsat/metadata and data/planetlab/metadata. Data that has been processed into training and validation datasets are saved as LevelDB files into data/leveldb.
Preparing the data, training, and generating graphs to know how we are doing is via a single command-line tool, cloudless.py.
To setup this tool, you must have CAFFE_HOME defined and have Caffe installed.
Second, ensure you have all Python requirements installed by going into the cloudless root directory and running:
pip install -r requirements.txt
Third, ensure you have ./src in your PYTHONPATH as well as the Python bindings for Caffe compiled and in your PYTHONPATH as well: export PYTHONPATH=$PYTHONPATH:/usr/local/caffe/python:./src
Run the following to see options:
./src/cloudless/cloudless.py --help
Training info and graphs go into logs/.
We currently have pretrained weights from the BVLC AlexNet Caffe Model Zoo, in src/caffe_model/bvlc_alexnet. This is trained on ILSVRC 2012, almost exactly as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012.
Note that the trained AlexNet file is much too large to check into Github (it's about 350MB). You will have to download the file from here and copy it to src/caffe_model/bvlc_alexnet/bvlc_alexnet.caffemodel.
The scripts to prepare the Landsat data are here.
The zip file is the raw imagery stuff pulled down from USGS.
The training-set.csv has 5 columns:
- Image Name
- Clouds - 1 if there are clouds
- Edge - 1 if the image is partially nulled out
- Blank - 1 if the image is totally nulled out
- Comments - any random comments I had
Download the cropped images (not the raw zip file) and place them into data/landsat/images, and copy the training-set.csv file to data/landsat/metadata/training-validation-set.csv.
Once you've prepped your datasets, the first step is to preprocess the data into the format required by Caffe, LevelDB:
./src/cloudless/cloudless.py -p