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awesome-deeplearning icon awesome-deeplearning

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

crowdcounting-p2pnet icon crowdcounting-p2pnet

The official codes for the ICCV2021 Oral presentation "Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework"

crowdcounting-uepnet icon crowdcounting-uepnet

The official codes for the ICCV2021 presentation "Uniformity in Heterogeneity: Diving Deep into Count Interval Partition for Crowd Counting"

deeplab2 icon deeplab2

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.

detectron2 icon detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

dino icon dino

Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"

dn-detr icon dn-detr

[CVPR 2022 Oral]Official implementation of DN-DETR

fcdd icon fcdd

Repository for the Explainable Deep One-Class Classification paper

k-net icon k-net

[NeurIPS2021] Code Release of K-Net: Towards Unified Image Segmentation

maskformer icon maskformer

Per-Pixel Classification is Not All You Need for Semantic Segmentation (NeurIPS 2021, spotlight)

panoptic-deeplab icon panoptic-deeplab

This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)

panopticfcn icon panopticfcn

Fully Convolutional Networks for Panoptic Segmentation (CVPR2021 Oral)

randla-net icon randla-net

🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

steps icon steps

This is the official repository for ICRA-2023 paper "STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation"

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