Spatial Transformer Nets in TensorFlow/ TensorLayer

Last update: Nov 23, 2022

Overview

MOVED TO HERE

Spatial Transformer Networks

Spatial Transformer Networks (STN) is a dynamic mechanism that produces transformations of input images (or feature maps)including scaling, cropping, rotations, as well as non-rigid deformations. This enables the network to not only select regions of an image that are most relevant (attention), but also to transform those regions to simplify recognition in the following layers.

Video for different transformation click me.

In this repositary, we implemented a STN for 2D Affine Transformation on MNIST dataset. We generated images with size of 40x40 from the original MNIST dataset, and distorted the images by random rotation, shifting, shearing and zoom in/out. The STN was able to learn to automatically apply transformations on distorted images via classification task.

Fig 1：Transformation

Fig 2：Network

Fig 3：Formula

Result

After classification task, the STN is able to transform the distorted image from Fig 4 back to Fig 5.

Fig 4: Input

Fig 5: Output

Comments

Export graph

Hello,

I'm trying to export this model and its weights into a frozen graph. So far I did this in the "save images" part of the training loop:

saver = tf.train.Saver() saver.save(sess, 'my_stn_model_' + str(epoch)) tf.train.write_graph(sess.graph_def, ".", "test.pb", False) #proto

Then I have my pb and the weights. But I am unable to generate a frozen graph from this because I cannot guess the output node in the graph (which is a really complex one it seems).

I attached the pb which I am able to visualize using Netron:

test.zip

Thanks in advance.

opened by hvico 0
Unexpected Output

I'm trying to train the STN. The training dataset which I've provided contains MNIST dataset images which are rotated 90 degree and the testing dataset contains simple MNIST dataset images. The output I'm getting is straight MNIST images. I was expecting the output to contain characters which are rotated 90 degrees. Can you please guide me on how this works?

opened by nabeel3133 4

Spatial Transformer Nets in TensorFlow/ TensorLayer

Related tags

Overview

MOVED TO HERE

Spatial Transformer Networks

Result

You might also like...

Group Activity Recognition with Clustered Spatial Temporal Transformer

VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree

data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer"

Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

Official PyTorch implementation of Spatial Dependency Networks.

Spatial Intention Maps for Multi-Agent Mobile Manipulation (ICRA 2021)

The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

Spatial Action Maps for Mobile Manipulation (RSS 2020)

Comments

Export graph

Unexpected Output

Releases(0.0.1)

0.0.1(Jun 26, 2017)

Owner

Hao

Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

Efficient neural networks for analog audio effect modeling

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Datasets, Transforms and Models specific to Computer Vision

The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.

Art Project "Schrödinger's Game of Life"

Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.

Official Pytorch implementation for 2021 ICCV paper "Learning Motion Priors for 4D Human Body Capture in 3D Scenes" and trained models / data

In-place Parallel Super Scalar Samplesort (IPS⁴o)

Metric learning algorithms in Python

Download and preprocess popular sequential recommendation datasets

VGGVox models for Speaker Identification and Verification trained on the VoxCeleb (1 & 2) datasets

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Pytorch Implementation of PointNet and PointNet++++

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

Affine / perspective transformation in Pose Estimation with Tensorflow 2

Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

[RSS 2021] An End-to-End Differentiable Framework for Contact-Aware Robot Design

Improving adversarial robustness by a coupling rejection strategy

The original weights of some Caffe models, ported to PyTorch.