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Deformable-DETR is an object detection system for computer vision that uses a transformer-based encoder-decoder architecture. It identifies and locates objects within images by representing potential targets as a set of learnable queries.
The main features of fundamentalvision/deformable-detr are: Deformable Attention, Object Detection, Detection Model Training, Multi-Scale Feature Pyramids, Transformer-Based Architectures, Object Query Mechanisms, Data-Parallel Training, Hungarian Matching Losses.
Projects with overlapping indexed features include: facebookresearch/detr — This project provides a transformer-based object detection model that treats the task as a direct set prediction… eriklindernoren/pytorch-yolov3 — This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time… microsoft/table-transformer — Table Transformer is a deep learning framework designed for document layout analysis and the automated extraction of… jwyang/faster-rcnn.pytorch — This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a… rbgirshick/py-faster-rcnn — This project is a Python implementation of the Faster R-CNN object detection framework. It serves as a convolutional… ultralytics/yolov3 — This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a…
This project provides a transformer-based object detection model that treats the task as a direct set prediction problem. It implements a vision system capable of predicting bounding boxes and class labels for objects within an image, as well as frameworks for instance and panoptic segmentation. The architecture utilizes a transformer encoder and decoder to perform end-to-end set prediction, employing a Hungarian matcher to assign predicted boxes to ground truth objects. It incorporates a convolutional backbone for feature extraction and a system of learnable object queries to probe image loc
This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels. The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications. The framework includes tools for model evaluation to m
Table Transformer is a deep learning framework designed for document layout analysis and the automated extraction of tabular data from unstructured images and documents. It utilizes a transformer-based architecture to perform object detection, identifying table boundaries and internal grid structures to convert visual information into machine-readable formats. The project distinguishes itself by employing a global optimization strategy for bipartite matching, which eliminates the need for traditional non-maximum suppression during the detection process. By leveraging multi-scale feature extra
This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa