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TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet
| Date | Stars |
|---|---|
| 2026-07-24 | 397 |
| 2026-07-25 | 397 |
| 2026-07-28 | 397 |
| 2026-07-30 | 397 |
| 2026-08-06 | 397 |
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# Keras implementation of [PSPNet(caffe)](https://github.com/hszhao/PSPNet)
Implemented Architecture of Pyramid Scene Parsing Network in Keras.
For the best compability please use Python3.5
### Setup
1. Install dependencies:
* Tensorflow (-gpu)
* Keras
* numpy
* scipy
* pycaffe(PSPNet)(optional for converting the weights)
```bash
pip install -r requirements.txt --upgrade
```
2. Converted trained weights are needed to run the network.
Weights(in ```.h5 .json``` format) have to be downloaded and placed into directory ``` weights/keras ```
Already converted weights can be downloaded here:
* [pspnet50_ade20k.h5](https://www.dropbox.com/s/0uxn14y26jcui4v/pspnet50_ade20k.h5?dl=1)
[pspnet50_ade20k.json](https://www.dropbox.com/s/v41lvku2lx7lh6m/pspnet50_ade20k.json?dl=1)
* [pspnet101_cityscapes.h5](https://www.dropbox.com/s/c17g94n946tpalb/pspnet101_cityscapes.h5?dl=1)
[pspnet101_cityscapes.json](https://www.dropbox.com/s/fswowe8e3o14tdm/pspnet101_cityscapes.json?dl=1)
* [pspnet101_voc2012.h5](https://www.dropbox.com/s/uvqj2cjo4b9c5wg/pspnet101_voc2012.h5?dl=1)
[pspnet101_voc2012.json](https://www.dropbox.com/s/rr5taqu19f5fuzy/pspnet101_voc2012.json?dl=1)
## Convert weights by yourself(optional)
(Note: this is **not** required if you use .h5/.json weights)
Running this needs the compiled original PSPNet caffe code and pycaffe.
```bash
python weight_converter.py <path to .prototxt> <path to .caffemodel>
```
## Usage:
```bash
python pspnet.py -m <model> -i <input_image> -o <output_path>
python pspnet.py -m pspnet101_cityscapes -i example_images/cityscapes.png -o example_results/cityscapes.jpg
python pspnet.py -m pspnet101_voc2012 -i example_images/pascal_voc.jpg -o example_results/pascal_voc.jpg
```
List of arguments:
```bash
-m --model - which model to use: 'pspnet50_ade20k', 'pspnet101_cityscapes', 'pspnet101_voc2012'
--id - (int) GPU Device id. Default 0
-s --sliding - Use sliding window
-f --flip - Additional prediction of flipped image
-ms --multi_scale - Predict on multiscale images
```
## Keras results:
## Implementation details
* The interpolation layer is implemented as custom layer "Interp"
* Forward step takes about ~1 sec on single image
* Memory usage can be optimized with:
```python
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
sess = tf.Session(config=config)
```
* ```ndimage.zoom``` can take a long time
Excerpt of 3,034 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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