Classifying cat and dog images using Kaggle dataset

Related tags

Deep Learningpytorch-image-classification
Overview

PyTorch Image Classification

Classifies an image as containing either a dog or a cat (using Kaggle's public dataset), but could easily be extended to other image classification problems.

Dependencies:

  • PyTorch / Torchvision
  • Numpy
  • PIL
  • CUDA

Data

The data directory structure I used was:

  • project
    • data
      • train
        • dogs
        • cats
      • validation
        • dogs
        • cats
      • test
        • test

Performance

The result of the notebook in this repo produced a log loss score on Kaggle's hidden dataset of 0.04988 -- further gains can probably be achieved by creating an ensemble of classifiers using this approach.

Owner
Robert Coleman
Robert Coleman
GitHub Repository
A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

TADDY: Anomaly detection in dynamic graphs via transformer This repo covers an reference implementation for the paper "Anomaly detection in dynamic gr

Yue Tan 21 Nov 24, 2022
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
A simple program for training and testing vit

Vit This is a simple program for training and testing vit. Key requirements: torch, torchvision and timm. Dataset I put 5 categories of the cub classi

xiezhenyu 2 Oct 11, 2022
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.

Van 21 Dec 30, 2022
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition.

TraND This is the code for the paper "Jinkai Zheng, Xinchen Liu, Chenggang Yan, Jiyong Zhang, Wu Liu, Xiaoping Zhang and Tao Mei: TraND: Transferable

Jinkai Zheng 32 Apr 04, 2022
In the case of your data having only 1 channel while want to use timm models

timm_custom Description In the case of your data having only 1 channel while want to use timm models (with or without pretrained weights), run the fol

2 Nov 26, 2021
Code for all the Advent of Code'21 challenges mostly written in python

Advent of Code 21 Code for all the Advent of Code'21 challenges mostly written in python. They are not necessarily the best or fastest solutions but j

4 May 26, 2022
Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".

SURGE: Sequential Recommendation with Graph Neural Networks This is our TensorFlow implementation for the paper: Sequential Recommendation with Graph

FIB LAB, Tsinghua University 53 Dec 26, 2022
PiRapGenerator - Make anyone rap the digits of pi

PiRapGenerator Make anyone rap the digits of pi (sample files are of Ted Nivison

7 Oct 02, 2022
An implementation of Deep Graph Infomax (DGI) in PyTorch

DGI Deep Graph Infomax (Veličković et al., ICLR 2019): https://arxiv.org/abs/1809.10341 Overview Here we provide an implementation of Deep Graph Infom

Petar Veličković 491 Jan 03, 2023
Voxel-based Network for Shape Completion by Leveraging Edge Generation (ICCV 2021, oral)

Voxel-based Network for Shape Completion by Leveraging Edge Generation This is the PyTorch implementation for the paper "Voxel-based Network for Shape

10 Dec 04, 2022
Single-Shot Motion Completion with Transformer

Single-Shot Motion Completion with Transformer 👉 [Preprint] 👈 Abstract Motion completion is a challenging and long-discussed problem, which is of gr

FuxiCV 78 Dec 29, 2022
Official implementation of Rich Semantics Improve Few-Shot Learning (BMVC, 2021)

Rich Semantics Improve Few-Shot Learning Paper Link Abstract : Human learning benefits from multi-modal inputs that often appear as rich semantics (e.

Mohamed Afham 11 Jul 26, 2022
Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER 🦌 🦒 Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEE

33 Dec 23, 2022
CT Based COVID 19 Diagnose by Image Processing and Deep Learning

This project proposed the deep learning and image processing method to undertake the diagnosis on 2D CT image and 3D CT volume.

1 Feb 08, 2022
Flappy bird automation using Neuroevolution of Augmenting Topologies (NEAT) in Python

FlappyAI Flappy bird automation using Neuroevolution of Augmenting Topologies (NEAT) in Python Everything Used Genetic Algorithm especially NEAT conce

Eryawan Presma Y. 2 Mar 24, 2022
A Python 3 package for state-of-the-art statistical dimension reduction methods

direpack: a Python 3 library for state-of-the-art statistical dimension reduction techniques This package delivers a scikit-learn compatible Python 3

Sven Serneels 32 Dec 14, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
Clean and readable code for Decision Transformer: Reinforcement Learning via Sequence Modeling

Minimal implementation of Decision Transformer: Reinforcement Learning via Sequence Modeling in PyTorch for mujoco control tasks in OpenAI gym

Nikhil Barhate 104 Jan 06, 2023
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

Haotong Qin 59 Dec 17, 2022