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

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Tensorflow-Project-Template - A best practice for tensorflow project template architecture.

Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Simple tutorials on Pytorch DDP training

A repository built on the Flow software package to explore cyber-security attacks on intelligent transportation systems.

Convnet transfer - Code for paper How transferable are features in deep neural networks?

Improving Calibration for Long-Tailed Recognition (CVPR2021)

Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation"

CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Offcial repository for the IEEE ICRA 2021 paper Auto-Tuned Sim-to-Real Transfer.

Python implementation of "Elliptic Fourier Features of a Closed Contour"

Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding

Heart Arrhythmia Classification

Caffe implementation for Hu et al. Segmentation for Natural Language Expressions

Manim is an engine for precise programmatic animations, designed for creating explanatory math videos

Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

PyTorch implementation of UPFlow (unsupervised optical flow learning)