TensorBox is a simple framework for training neural networks to detect objects in images. Training requires a json file (e.g. here) containing a list of images and the bounding boxes in each image. The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm, reproducing state-of-the-art detection results on the highly occluded TUD crossing and brainwash datasets.
First, install TensorFlow from source or pip (NB: source installs currently break threading on 0.11)
$ git clone http://github.com/russell91/tensorbox
$ cd tensorbox
$ ./download_data.sh
$ cd /path/to/tensorbox/utils && make && cd ..
$ python train.py --hypes hypes/overfeat_rezoom.json --gpu 0 --logdir output
Note that running on your own dataset should only require modifying the hypes/overfeat_rezoom.json file.
When finished training, you can use code from the provided
ipython notebook
to get results on your test set.
ReInspect, initially implemented in Caffe, is a neural network extension to Overfeat-GoogLeNet in Tensorflow. It is designed for high performance object detection in images with heavily overlapping instances. See the paper for details or the video for a demonstration.
# REQUIRES TENSORFLOW VERSION >= 0.8
$ git clone http://github.com/russell91/tensorbox
$ cd tensorbox
$ ./download_data.sh
$ # Download the cudnn version used by your tensorflow verion and
$ # put the libcudnn*.so files on your LD_LIBRARY_PATH e.g.
$ cp /path/to/appropriate/cudnn/lib64/* /usr/local/cuda/lib64
$ cd /path/to/tensorbox/utils && make && make hungarian && cd ..
$ python train.py --hypes hypes/lstm_rezoom.json --gpu 0 --logdir output
You can visualize the progress of your experiments during training using Tensorboard.
$ cd /path/to/tensorbox
$ tensorboard --logdir output
$ # (optional, start an ssh tunnel if not experimenting locally)
$ ssh myserver -N -L localhost:6006:localhost:6006
$ # open localhost:6006 in your browser
For example, the following is a screenshot of a Tensorboard comparing two different experiments with learning rate decays that kick in at different points. The learning rate drops in half at 60k iterations for the green experiment and 300k iterations for red experiment.
