PSPNet_tensorflow

Introduction

This is an implementation of PSPNet in TensorFlow for semantic segmentation on the cityscapes dataset. We first convert weight from Original Code by using caffe-tensorflow framework.

Update:

2018/01/19:

Support inference phase for ade20k dataset using model of pspnet50 (convert weights from original author)
Using tf.matmul to decode label, so as to improve the speed of inference.

2017/11/06:

Support different input size by padding input image to (720, 720) if original size is smaller than it, and get result by cropping image in the end.

2017/10/27:

Change bn layer from tf.nn.batch_normalization into tf.layers.batch_normalization in order to support training phase. Also update initial model in Google Drive.

Install

Get restore checkpoint from Google Drive and put into model directory. Note: Select the checkpoint corresponding to the dataset.

Inference

To get result on your own images, use the following command:

python inference.py --img-path=./input/test.png --dataset cityscapes

Inference time: ~0.6s

Options:

--dataset cityscapes or ade20k
--flipped-eval 
--checkpoints /PATH/TO/CHECKPOINT_DIR

Evaluation

Perform in single-scaled model on the cityscapes validation datase.

Method	Accuracy
Without flip	76.99%
Flip	77.23%

To get evaluation result, you need to download Cityscape dataset from Official website first. Then change DATA_DIRECTORY to your dataset path in evaluate.py:

DATA_DIRECTORY = /Path/to/dataset

Then run the following command:

python evaluate.py

List of Args:

--flipped-eval  - Using flipped evaluation method
--measure-time  - Calculate inference time

Image Result

cityscapes

Input image	Output image

ade20k

Input image	Output image

real world

Input image	Output image

guo2004131 / PSPNet-tensorflow