fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

Invert and perturb GAN images for test-time ensembling

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

Repositório dos exemplos e desafios utilizados na disciplina de Visão Computacional do curso de MBA Machine Learning da FIAP

Scaling Object Detection by Transferring Classification Weights

A lightweight and extensible toolbox for image classification and MORE

The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction

Code release for "A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation", accepted by CVPR2023.

Web Based Image Recognition System in Python Flask

Deploy AutoML models for image classification on AWS Sagemaker with AutoGluon

This repository contains demonstrations done with deep learning computer vision models.

[CVPR 2019] SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network

Protein localization in cell microscopy images Kaggle competition

Image Classification with Convolutional Neural Networks in TensorFlow: Fashion Item Classifier

Awesome list of papers on sea ice, covering semi-supervised learning and supervised learning

CenOTAPH: COlour and Texture Analysis toolbox for PytHon

Cat-Dog classifier based on pre-trained model (MobileNet)

🍎 Fruits Classification App (Gradio) 🍌

Implementations of various classifiers on MNIST dataset. Both traditional machine learning and deep learning methods are included.

An interpretability adapter on top of a vision foundation backbone that learns sparse, class-independent features for globally interpretable image classification.