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fundamentalvision avatar

fundamentalvision/Deformable-DETR

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3,895 stele·612 fork-uri·Python·apache-2.0·5 vizualizări

Deformable DETR

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 project employs sampling-based attention to restrict attention to a small set of points around a reference, reducing computational complexity and speeding up convergence. It further utilizes multi-scale feature fusion to detect objects of varying sizes within a single frame.

The system includes capabilities for training models across multiple GPU clusters using distributed data parallelism and evaluating detection precision against standard benchmark datasets.

Features

  • Deformable Attention - Implements deformable attention by sampling a small set of points around a reference to reduce complexity.
  • Object Detection - Identifies and locates specific objects within images using bounding boxes and classification.
  • Detection Model Training - Trains deep learning models to identify and locate multiple objects within images using transformer architectures.
  • Multi-Scale Feature Pyramids - Uses multi-scale feature pyramids to detect objects of varying sizes within a single image frame.
  • Transformer-Based Architectures - Utilizes a transformer-based encoder-decoder architecture to generate object queries for set prediction.
  • Object Query Mechanisms - Employs learnable object queries that interact via attention to predict bounding boxes and object classes.
  • Data-Parallel Training - Provides data-parallel training to synchronize gradients across multiple GPU nodes for faster convergence.
  • Hungarian Matching Losses - Implements bipartite matching losses to assign predicted boxes to ground truth objects without hand-crafted anchors.
  • GPU Training Accelerators - Accelerates training speed by parallelizing workloads across multiple GPU clusters.
  • Distributed Training - Scales the training of the object detection model across multiple compute nodes.
  • Object Detection - Deformable transformers for end-to-end object detection.
  • Object Detection and Segmentation - Deformable transformers for end-to-end object detection.

Istoric stele

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Întrebări frecvente

Ce face fundamentalvision/deformable-detr?

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.

Care sunt principalele funcționalități ale fundamentalvision/deformable-detr?

Principalele funcționalități ale fundamentalvision/deformable-detr sunt: Deformable Attention, Object Detection, Detection Model Training, Multi-Scale Feature Pyramids, Transformer-Based Architectures, Object Query Mechanisms, Data-Parallel Training, Hungarian Matching Losses.

Care sunt câteva alternative open-source pentru fundamentalvision/deformable-detr?

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