Fashion Entity Classification

Last update: Jan 04, 2022

Overview

Fashion-Entity-Classification

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

Labels

Each training and test example is assigned to one of the following labels:

0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat
5 Sandal
6 Shirt
7 Sneaker
8 Bag
9 Ankle boot

Each row is a separate image
Column 1 is the class label.
Remaining columns are pixel numbers (784 total).
Each value is the darkness of the pixel (1 to 255)

Results

Owner

ADITYA SHAH

#IndianByHeart #MechatronicsEnthusiast #NatureLover #Wanderer

GitHub Repository

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Fashion Entity Classification

Related tags

Overview

Fashion-Entity-Classification

Each row is a separate image
Column 1 is the class label.
Remaining columns are pixel numbers (784 total).
Each value is the darkness of the pixel (1 to 255)

Owner

ADITYA SHAH

LSTM-VAE Implementation and Relevant Evaluations

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

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COIN the currently largest dataset for comprehensive instruction video analysis.

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

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Fashion Entity Classification

Related tags

Overview

Fashion-Entity-Classification

Each row is a separate image Column 1 is the class label. Remaining columns are pixel numbers (784 total). Each value is the darkness of the pixel (1 to 255)

Owner

ADITYA SHAH

LSTM-VAE Implementation and Relevant Evaluations

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

Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

Official Pytorch implementation of Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

COIN the currently largest dataset for comprehensive instruction video analysis.

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Anchor-free Oriented Proposal Generator for Object Detection

An implementation of "Learning human behaviors from motion capture by adversarial imitation"

Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)

buildseg is a building extraction plugin of QGIS based on PaddlePaddle.

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

TransReID: Transformer-based Object Re-Identification

Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''.

PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

李云龙二次元风格化!打滚卖萌，使用了animeGANv2进行了视频的风格迁移

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[AAAI 2022] Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

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Each row is a separate image
Column 1 is the class label.
Remaining columns are pixel numbers (784 total).
Each value is the darkness of the pixel (1 to 255)