https://github.com/Vectorized/VoxNet-Tensorflow
A Tensorflow Implementation of VoxNet.
https://github.com/Vectorized/VoxNet-Tensorflow
Last synced: 10 months ago
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A Tensorflow Implementation of VoxNet.
- Host: GitHub
- URL: https://github.com/Vectorized/VoxNet-Tensorflow
- Owner: Vectorized
- Created: 2018-08-02T07:07:08.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-02T07:21:08.000Z (over 7 years ago)
- Last Synced: 2024-10-23T15:13:19.959Z (over 1 year ago)
- Language: Python
- Size: 60.3 MB
- Stars: 11
- Watchers: 3
- Forks: 7
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
# VoxNet Tensorflow
A Tensorflow Implementation of VoxNet (http://dimatura.net/research/voxnet/).
## Pre-trained Weights
Pre-trained weights are provided under the `checkpoints` directory.
This network is pretty small, under 1 million parameters, which is less than 10MB in size.
## Dataset
A pre-voxelized ShapeNet40 (http://modelnet.cs.princeton.edu/) is already provided in `volumetric_data.zip`.
It is directly converted from the matlab files included in (http://vision.princeton.edu/projects/2014/3DShapeNets/3DShapeNetsCode.zip) for fairness.
Each example is a .npy file. To visualize a voxel .npy file, go to (http://bkys.io/voxvis).
## Modifications
No dropout is used for faster training.
Batch normalization is used after every layer, except the last fully connected layer.
Network is all-convolutional.
## Requirements
Python (>= 2.7 or >= 3), Tensorflow (>= 1.0) and numpy (>= 1.0).
## How To Use
Run `voxnet_train.py` to train the model. (You can skip this step if you trust the pre-trained model)
Run `voxnet_test.py` to test the average accuracy.
If you want to test on a single file, you have to extract it from the `volumetric_data.zip` and modify the code.
## Accuracy
After 20 minutes of training from scratch on a Nvidia Titan X, it is able to get 86%+ test accuracy.