Awesome GitHub RepositoriesVision Model Training

This project is a library of pretrained computer vision architectures and backbones for image classification and feature extraction. It serves as a comprehensive model zoo and collection of standardized image encoders, including ResNet, Vision Transformers, and EfficientNet, for use in visual analysis and as backbones for object detection and image segmentation. The library provides a framework for distributed training and evaluation of image models using advanced data augmentation and optimization scripts. It includes a dedicated toolset for converting trained PyTorch vision models into the

Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati

This project is a deep learning image restoration tool designed to remove scratches, fading, and noise from aged photographs and film. It utilizes generative adversarial networks for image translation, alongside specialized networks for face enhancement and video colorization. The system distinguishes itself through a combination of latent-space domain mapping and progressive face enhancement to recover blurred or missing high-frequency facial details. For video content, it employs a colorization framework that uses optical flow and temporal guidance to propagate color from selected keyframes

Open CLIP is an open source framework for training and deploying Contrastive Language-Image Pre-training models. It serves as a vision-language training framework and multimodal embedding engine that maps images and text into a shared vector space for similarity searches and zero-shot classification. The project provides a toolkit for distributed training of contrastive models and includes an image-to-text generative model for producing natural language descriptions. It supports custom text encoder integration and utilizes teacher-student model distillation to transfer knowledge from large pr

This project is a self-supervised vision foundation model based on a vision transformer architecture. It is designed to learn dense visual representations from unlabeled images, serving as a general-purpose backbone for a wide variety of downstream vision tasks. The system is distinguished by its use of self-distillation and masked image modeling to extract semantic and geometric features. It also incorporates an image-text alignment model that maps visual embeddings to textual descriptions, enabling zero-shot image recognition, zero-shot segmentation, and cross-modal retrieval. The project

YOLOv9 is a real-time computer vision framework and deep learning model designed for image classification, object detection, and instance segmentation. It functions as both a vision model and a trainer, allowing for the optimization of neural network weights on custom datasets using single or multiple GPUs. The framework utilizes programmable gradient information to perform high-speed identification and location of multiple objects within images and video streams. It extends beyond bounding box detection to provide instance segmentation and panoptic segmentation, which labels every pixel in a

Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single RGB image. It is built on a DINOv2 Vision Transformer encoder backbone and trained on 62 million unlabeled images using a teacher-student pseudo-labeling framework, enabling robust generalization across diverse scenes without task-specific training. The model outputs both relative depth maps, which capture the ordering of scene points, and metric depth maps with real-world units after fine-tuning on datasets like NYUv2 or KITTI. The project distinguishes itself through its ab

This project is a collection of educational resources and instructional guides for learning deep learning and neural network implementation using TensorFlow. It provides a structured set of tutorials and notebooks written in Chinese, covering supervised and unsupervised learning tasks. The material focuses on practical implementations of diverse neural network architectures, including convolutional, recurrent, and autoencoder networks. It includes specific training content for computer vision, natural language processing, and generative models. The coverage extends to specialized network arc

LARK is a development toolkit for training, fine-tuning, and deploying large language models and multimodal models based on PaddlePaddle. It functions as a comprehensive framework that includes an LLM training orchestrator, an inference server, and a multimodal model framework for processing text, image, and video inputs. The project features a retrieval-augmented generation system for building conversational applications that integrate web search and private knowledge bases. It provides specific capabilities for multimodal reasoning and complex logic, enabling the extraction of structured da

This project is a PyTorch vision transformer framework designed for self-supervised learning. It implements a model that trains visual representations using a momentum teacher and self-distillation without the need for labeled data. The library functions as an image feature extractor and visual attention visualizer, allowing for the generation of high-dimensional vectors and the rendering of self-attention maps as heatmaps or videos to analyze model focus. It provides comprehensive tools for downstream vision evaluation, including linear probe classification, k-nearest neighbor categorizatio

Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in gene

CogVLM is a multimodal large language model designed to integrate visual and textual data for reasoning about images and generating natural language. It functions as a visual question answering system that analyzes image content to provide detailed descriptions or answer specific questions. The project includes a visual grounding model capable of mapping text descriptions to precise bounding box coordinates within an image. It also features a vision-based automation agent that analyzes screen captures to generate execution plans and interaction coordinates for software interfaces. The system

यह विविध नेचुरल लैंग्वेज प्रोसेसिंग कार्यों को एक एकीकृत टेक्स्ट-टू-टेक्स्ट समस्या के रूप में मानने के लिए एक मशीन लर्निंग फ्रेमवर्क है। यह बड़े पैमाने पर ट्रांसफॉर्मर मॉडल को प्री-ट्रेन और फाइन-ट्यून करने के लिए एक टूलकिट प्रदान करता है, जो एक ऐसी प्रणाली का उपयोग करता है जहां इनपुट और आउटपुट दोनों को रॉ टेक्स्ट अनुक्रमों के रूप में स्वरूपित किया जाता है। फ्रेमवर्क अपने डिस्ट्रीब्यूटेड ट्रेनिंग सिस्टम के लिए जाना जाता है, जो कई TPU कोर में मॉडल वेट्स और ट्रेनिंग बैच को स्केल करने के लिए मेश-आधारित रणनीतियों का उपयोग करता है। यह कॉन्फ़िगर करने योग्य मिश्रण दरों का उपयोग करके विविध डेटासेट को एक ही ट्रेनिंग स्ट्रीम में जोड़कर मल्टी-टास्क लर्निंग का समर्थन करता है, जिससे एक ही मॉडल विभिन्न भाषा कार्यों को संभालने में सक्षम होता है। सिस्टम एनकोडर-डिकोडर आर्किटेक्चर, टेक्स्ट जनरेशन के लिए बीम-सर्च डिकोडिंग, और ट्रांसफर लर्निंग वर्कफ़्लो सहित क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है। इसमें NLP डेटासेट तैयारी, मॉडल प्रदर्शन मूल्यांकन, और प्रोडक्शन सर्विंग के लिए प्रशिक्षित चेकपॉइंट्स को एक्सपोर्ट करने के लिए उपयोगिताएँ शामिल हैं। लाइब्रेरी विकास में तेजी लाने के लिए विभिन्न आकारों के प्री-ट्रेंड मॉडल चेकपॉइंट्स को लोड करने का समर्थन करती है।

ByteTrack is a multi-object tracking framework that implements the ByteTrack algorithm, an ECCV 2022 method designed to recover occluded objects and reduce trajectory fragmentation. The core innovation of the project is its association algorithm, which processes every detection box—including low-confidence ones—by using separate high and low score thresholds, Kalman filter motion prediction, and Hungarian algorithm matching to produce consistent object identities across video frames. The project distinguishes itself by its comprehensive approach to handling occlusions and fragmented trajector

YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on

Tensorpack एक उच्च-स्तरीय TensorFlow न्यूरल नेटवर्क फ्रेमवर्क और रिसर्च लाइब्रेरी है जिसे डीप लर्निंग मॉडल्स बनाने और प्रशिक्षित करने के लिए डिज़ाइन किया गया है। यह कंप्यूटर विज़न, जनरेटिव टास्क, रीइन्फोर्समेंट लर्निंग और नेचुरल लैंग्वेज प्रोसेसिंग के लिए पुनरुत्पादक (reproducible) न्यूरल नेटवर्क आर्किटेक्चर का संग्रह प्रदान करता है। प्रोजेक्ट एक विशेष डीप लर्निंग डेटा पाइपलाइन के माध्यम से खुद को अलग करता है जो समानांतर डेटा लोडिंग और स्ट्रीमिंग के लिए शुद्ध Python का उपयोग करती है। इसमें डेटा-समानांतर रणनीतियों के माध्यम से वर्कलोड वितरित करने के लिए एक मल्टी-GPU ट्रेनिंग ऑर्केस्ट्रेटर और मॉडल सेलियंसी और एक्टिवेशन मैप्स को विज़ुअलाइज़ करने के लिए एक समर्पित इंटरप्रिटेबिलिटी टूलकिट शामिल है। फ्रेमवर्क क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है, जिसमें ऑब्जेक्ट डिटेक्शन और सिमेंटिक सेगमेंटेशन के लिए कंप्यूटर विज़न पाइपलाइन्स, स्पीच और टेक्स्ट के लिए अनुक्रम मॉडलिंग, और रीइन्फोर्समेंट लर्निंग एजेंट विकास शामिल है। यह वेट क्वांटाइज़ेशन और लो बिटविड्थ ट्रेनिंग के लिए मॉडल ऑप्टिमाइज़ेशन उपकरण भी प्रदान करता है, साथ ही शैक्षणिक शोध पत्रों को पुनरुत्पादित करने और लीगेसी Caffe मॉडल वेट्स को परिवर्तित करने के लिए यूटिलिटीज भी प्रदान करता है।

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate