Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
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Updated
Sep 30, 2024 - Python
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
A paper list of some recent Transformer-based CV works.
Transformer architecture conditioned by metadata
SA-DETR: Saliency Attention-based DETR for Salienct Object Detection
Implementation of popular deep learning networks with TensorRT network definition API
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
[ECCV2024 Oral] Official implementation of the paper "Relation DETR: Exploring Explicit Position Relation Prior for Object Detection"
[ECCV 2024] PyTorch implementation of Rethinking Features-Fused-Pyramid-Neck for Object Detection
Official pytorch repository for CG-DETR "Correlation-guided Query-Dependency Calibration in Video Representation Learning for Temporal Grounding"
OpenMMLab Detection Toolbox and Benchmark
A tool to convert bank statements into Excel files
Implementation of DETR in JAX
detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.
[CVPR 2024] Official implementation of the paper "Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement"
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
This repository is dedicated to comparing and benchmarking state-of-the-art Convolutional Neural Networks (CNNs) for detecting gunshot holes in various surfaces and materials.
SOTA Detection models
Handwritten text recognition using transformers.
細胞検知モデル作成を行うチュートリアルスクリプト
[NIPS2023] This is an official implementation of paper "DAC-DETR: Divide the Attention Layers and Conquer".
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