Image Classification
Showing 36 of 52 projects
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A comprehensive collection of PyTorch image models, layers, utilities, and training scripts for computer vision research and applications.
A fast and flexible Python library for image augmentation in computer vision tasks like classification, segmentation, and object detection.
A drop-in replacement for the MNIST dataset, featuring 70,000 Zalando fashion article images for benchmarking machine learning algorithms.
A Python library for building custom machine learning models for tasks like image classification, object detection, and recommendations.
A JavaScript library for client-side NSFW image detection using TensorFlow.js.
A hands-on beginner's guide to machine learning and image classification using Caffe and DIGITS with neural networks.
A web application for training deep learning models with a focus on computer vision tasks.
A browser-based tool that lets anyone create machine learning models without writing code, using TensorFlow.js.
A deep learning framework for training image classification models to solve complex captcha and OCR tasks.
A curated list of deep learning image classification papers and their code implementations since 2014.
A TensorFlow-based CNN solution for recognizing character-based CAPTCHAs, providing training, validation, and API modules.
A deep learning project using Keras to build convolutional and recurrent neural networks for high-accuracy captcha recognition.
A JavaScript application framework for machine learning and its engineering, designed for Web developers.
An open-source deep learning API and server written in C++ that supports multiple backends like PyTorch, TensorRT, and TensorFlow for training and inference.
A lightweight CoreML model for detecting NSFW content in images, specifically trained to distinguish between suggestive and explicit content.
Official repository for Big Transfer (BiT) models, providing pre-trained visual representations for efficient transfer learning across computer vision tasks.
A MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications.
A neural network built in Swift Playgrounds that learns to recognize hand-drawn emojis from 8x8 pixel images.
An open-source library providing chest X-ray datasets, pre-trained models, and tools for medical imaging research and analysis.
A collection of TensorFlow tutorials and examples covering image classification, GANs, text classification, and model deployment.
A library of modular computer vision components built on Keras 3, supporting TensorFlow, JAX, and PyTorch backends.
High-level TensorFlow network definitions with pre-trained weights for easy integration into existing ML workflows.
Deep learning inference nodes for ROS/ROS2 with support for NVIDIA Jetson devices and TensorRT.
A convolutional network-based image classifier and feature extractor trained on ImageNet, providing dense feature extraction capabilities.
A deep learning system that classifies food images into 230 categories and retrieves matching recipes using convolutional neural networks.
A curated collection of open-source machine learning models compatible with Apple's Core ML framework.
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
A fast C++ GPU implementation of Convolutional Neural Networks with multi-GPU support.
Winning solution for the Galaxy Challenge on Kaggle, using convolutional neural networks to classify galaxy morphologies.
Classify images offline on iOS using Watson Visual Recognition trained models and Apple's Core ML framework.
A PyTorch implementation of TResNet, a high-performance convolutional neural network architecture optimized for GPU training and inference.
A Pascal-based deep learning neural network API optimized for AVX/AVX2/AVX512 and OpenCL, supporting AMD, Intel, and NVIDIA hardware.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
TensorFlow implementation of weakly-supervised object localization using only image-level labels, without bounding box annotations.
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