This is a reimplementation of Bichen Wu's SqueezeDet [1],[2] for Theano [3] and Lasagne [4] by @corvidim.
SqueezeDet is particularly well-suited to low-power, low-memory-consumption applications. In particular, the trained weights take up less than 8Mb!
This release includes:
-
Network weights trained on the KITTI dataset (
data/squeezedet_kitti.pkl.gz). -
The detector ("prediction graph" and "interpretation graph" of the original TensorFlow implementation); training is not yet supported.
References:
- [1] https://arxiv.org/abs/1612.01051
- [2] https://github.com/BichenWuUCB/squeezeDet
- [3] http://deeplearning.net/software/theano/
- [4] http://lasagne.readthedocs.io/en/latest/
Requirements:
- Python 2.7+ (Python 3 experimental support; tested under Python 3.6.3)
- Theano 0.9+
- Lasagne 0.2+
- Numpy, PIL, Matplotlib
Sample docker files for Ubuntu installation:
./setup/sqzdet_theano_dockerfile.txt(Python 2, Theano 0.10)./setup/sqzdet_theano_0.9_dockerfile.txt(Python 2, Theano 0.9)./setup/sqzdet_theano_py3k_dockerfile.txt(Python 3, Theano 0.10)
To run detection on a default image (data/sample.png from the original SqueezeDet demo):
THEANO_FLAGS='floatX=float32' python src/sqz_det_thn.py --network_weights=data/squeezedet_kitti.pkl.gz
Use the --img_in flag to specify an image file, for example:
THEANO_FLAGS='floatX=float32' python src/sqz_det_thn.py --network_weights=data/squeezedet_kitti.pkl.gz --img_in=data/Boston_01.png
or specify a comma-separated list of images (all of which must be the same size), for example:
THEANO_FLAGS='floatX=float32' python src/sqz_det_thn.py --network_weights=data/squeezedet_kitti.pkl.gz --img_in=data/sample.png,data/Boston_00.png
Output visualization(s) will be saved in PDF format to the current
directory (e.g., ./out_sample.pdf) and displayed (if running
interactively) as shown below. To specify a different directory for
output use the --out_dir flag.
