#Object Detection

This PyTorch-based implementation of the Single Shot MultiBox Detector offers a streamlined approach for efficient object detection. Compatible with popular datasets and offering straightforward processes for setup, training, and evaluation, this project supports NVIDIA GPU acceleration and real-time training performance enhancements via Visdom integration. Users can explore transfer learning with pre-trained model weights, supported by comprehensive instructions for both command-line and Jupyter notebook demos. Regular updates aim to expand capabilities, including support for SSD512 and custom dataset training.

Explore a meticulously curated repository focused on Vision Transformer and Attention, featuring comprehensive resources like papers, codes, and links to relevant websites. Maintained by Min-Hung Chen, this updated list invites contributions to enhance its comprehensiveness and includes the latest developments from major conferences such as NeurIPS 2023 and ICCV 2023. Researchers can contribute by opening issues or creating pull requests for any missed papers, ensuring a continually relevant resource for academic and enthusiast communities alike.

The toolkit efficiently facilitates the deployment of AI models such as object and face detection, along with segmentation. Compatible with ONNXRuntime, TensorRT, and MNN, it provides platform-wide flexibility and performance. Its simple syntax and minimal dependencies endear it to developers, further complemented by extensive model support. Being open-source ensures continuous community enhancements.

Discover DETR's novel object detection method using Transformers, ensuring efficient and parallel predictions with reduced complexity. Learn through PyTorch examples and explore its application in computer vision.

This project uses Grounding DINO and Segment Anything to detect and segment objects with text prompts. Offering a pipeline for complex visual tasks with interchangeable models, it includes updates like Grounded SAM 2 and Grounding DINO 1.5. It supports platforms like Huggingface and Colab for exploring demos and technical insights, suitable for users interested in automated data annotation and enhanced visual task versatility.

This collection offers a variety of resources for deep learning-based object detection and tracking. It includes research papers addressing both static and dynamic detection with methods like RCNN, YOLO, SSD, and RetinaNet. The resource set also expands into multi and single object tracking techniques and provides specific datasets for tasks including UAV and microscopy tracking, as well as video segmentation and motion prediction. Comprehensive code repositories and frameworks are available to assist researchers and engineers in achieving efficient and state-of-the-art results in computer vision.

Discover deep learning techniques structured for satellite and aerial image analysis, covering classification, segmentation, and object detection among others. This resource details architectures, models, and algorithms devised to tackle the unique challenges posed by large image sizes and varied object classes. Uncover methods such as regression, cloud and change detection, time series analysis, and crop classification, emphasizing practical applications in remote sensing.

This repository is a comprehensive resource hub for the YOLO framework, renowned for real-time object detection. It offers official implementations and variations for platforms like PyTorch and TensorFlow. The project includes extensional frameworks, lightweight deployment options, and applications across diverse fields such as video and medical detection. Covering techniques like pruning, knowledge distillation, and quantization, it supports deployment on hardware like FPGA and TPU. Developers can benefit from the curated learning resources, paper reviews, and code evaluations to enhance skills relevant to fields like autonomous driving and robotics.

The project offers a detailed guide for leveraging ONNX and YOLOv8 in object detection, covering essential requirements and installation procedures, with a focus on GPU compatibility using onnxruntime-gpu for NVIDIA devices. It facilitates model conversion via Google Colab using clear Python scripts. The repository features examples for image, webcam, and video inference, demonstrating the model's versatility and efficiency. This project serves as a valuable resource for developers interested in implementing YOLOv8 in ONNX format, providing practical application insights.

Masked Image Consistency (MIC) advances unsupervised domain adaptation by focusing on spatial context relations in target domains. Through consistency between masked image predictions and pseudo-labels, MIC enhances visual recognition performance in tasks like image classification, semantic segmentation, and object detection. Suitable for various UDA challenges, including synthetic-to-real and clear-to-adverse-weather scenarios, MIC achieves high performance in benchmarks such as GTA to Cityscapes and VisDA-2017, contributing significantly to domain adaptation research.

CVNets is a dynamic computer vision toolkit designed for training various models like EfficientNet, Swin Transformer, and CLIP on tasks such as classification, detection, and segmentation. The toolkit's latest update includes features like Bytes Are All You Need and RangeAugment to boost model efficiency. Suitable for use by researchers and engineers, it offers comprehensive documentation and examples, including model conversion to CoreML.

YOLOv6 is an adaptive object detection framework built for industrial scalability. It features advanced segmentation, mobile capabilities, and optimized performance across various hardware platforms, including low-power devices. This robust single-stage detection model ensures versatile deployment and seamless integration into existing systems, ideal for real-time processing and large-scale tasks, prioritizing accuracy and reliability in demanding environments.

This curated list presents diverse PyTorch resources, covering tutorials, projects, and research materials on areas like language models, object detection, and neural optimization. It's a valuable reference for learning about PyTorch applications and advancements.

Discover methods to integrate TensorFlow into Android apps for sophisticated object detection. This project offers a detailed guide on developing TensorFlow libraries tailored for Android, featuring real-world application through camera-based image detection. Suitable for developers looking to augment Android apps with machine learning features using TensorFlow.

VisionScript is an abstract Python-based language designed for easy computer vision tasks like object detection, classification, and segmentation. It features a concise syntax allowing rapid implementation in just a few lines of code. Suitable for newcomers, it supports REPL and interactive notebooks, integrating models such as CLIP and YOLOv8. With straightforward installation, VisionScript empowers developers to quickly engage in computer vision projects, featuring lexical inference for improved workflow efficiency.

Explore the progressive field of AI through this detailed resource featuring DNN, GAN, NLP, and Big Data. Aimed at deep learning application engineers, it includes information on AI domains such as image classification advancements from LeNet to ResNet and Inception models. This project supports learning through articles, code, and visuals, facilitating understanding of cutting-edge technology and engineering project nuances. Developed by experienced programmers, it provides valuable resources for keeping up with and applying modern AI developments.

Leveraging TensorFlow 2.0, this YOLOv3 implementation offers efficient object detection models with pre-trained weights ready for deployment and customizable training. Supporting both eager and graph modes, and enhanced with GPU acceleration and absl-py integration, it adheres to industry best practices. It facilitates transfer learning, inference, and video detection, supported by detailed training resources. Darknet weight conversion and TensorFlow Serving integration make it suitable for academic and practical use.

Explore an extensive compilation of essential articles and resources on object detection, highlighting influential models such as R-CNN, YOLO, and SSD. This collection spans advancements from 2013 to 2019, providing insights into deep learning developments in object detection through papers, code, and comprehensive surveys. Examine subjects like real-time object detection, zero-shot detection, and weakly supervised methodologies, offering valuable information for those interested in cutting-edge object detection technologies.

Access a wide array of tutorials on leading computer vision models and methodologies. The repository includes education on models from ResNet and YOLO to more sophisticated types like DETR, Grounded DINO, SAM, and GPT-4 Vision. Gain knowledge on model fine-tuning, instance segmentation, and using object-detection transformers. Through thorough tutorials and practical examples, this resource supports both novices and experts in enhancing their skills across various vision tasks, leveraging platforms like Colab and Kaggle. Empower your computer vision initiatives with comprehensive tutorials and experiments available here.

T-Rex2 utilizes both text and visual prompts to improve object detection, enabling zero-shot detection applicable to various industries without prior labeling. Features like expanded YOLO format export enhance user accessibility, aiding dataset creation. T-Rex Label and the Count Anything APP exemplify its adaptability in handling complex industrial tasks. The project provides open API access for educators and researchers to advance their work in sectors such as agriculture, biology, and OCR. Discover the demo and API documentation for detailed application.

Utilizing Masked Image Modeling with a Vanilla ViT, this project enhances object detection and instance segmentation. A compact convolutional stem is integrated for multi-scale representation, forming a hybrid ViT-ConvNet backbone. It achieves significant results on COCO with 51.7 box AP and 46.2 mask AP, showcasing efficiency in training and accuracy in inference through varied sample ratios.

This tutorial guides on constructing object detection models with PyTorch, beginning with essential concepts in PyTorch and convolutional networks. It explains the SSD implementation, covering crucial elements like Multiscale Feature Maps and Priors, while offering insights into its architecture. Discover methods to improve model efficiency, such as Hard Negative Mining and Non-Maximum Suppression. Enhanced with practical examples and annotated images, this guide highlights model structure comprehension and prediction optimization.