Example application for training Google's pretrained Vision Transformer model on an image classification task with a large number of labels
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Updated
Feb 27, 2022 - Python
Example application for training Google's pretrained Vision Transformer model on an image classification task with a large number of labels
Image Augmentation Techniques
A lightweight, high-performance toolkit for generating robust image datasets. Features a Streamlit-based UI for real-time augmentation previews and batch processing, specifically optimized for document scanning, OCR, and computer vision workflows.
Classifying the paintings of the 50 greatest artists in history using a convolutional neural network
Image augmentation for machine learning experiments.
PlateDetectAI is a project using a custom YOLOv3 model for accurate vehicle number plate detection, featuring advanced image augmentation and custom loss functions.
This repository contains code and dataset for a multiclass image classification model to detect monkeypox using the ResNet50 architecture. The project focuses on classifying skin lesion images into different categories related to monkeypox, achieving 100% accuracy.
Classification of Sober and Intoxicated Faces using Image Analysis
Course Project for CSCI 5253: Image Augmentation as a Service
Image augmentation extension for the image-dataset-converter library.
Emotion recognition from facial images using convolutional neural networks.
This repository implements a method to map image correspondence from image pair augmentations.
An images augmentation program for yolo with labels auto adaptive
🖼️ Python-based image augmentation pipeline for YOLO object detection datasets using Albumentations. Applies 50+ augmentation transforms (weather effects, blur, geometric, color) while preserving bounding box annotations in YOLO format.
This project is to build a classification model by transfer learning.
SimplePY is a collection of Python scripts and utilities for file manipulation tailored for AI and ML projects. This repository aims to simplify the handling of datasets, model outputs, and other files commonly encountered in AI and machine learning workflows.
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