https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Depth Estimation
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Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cleinc/bts
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
https://github.com/clvrai/representation-learning-by-learning-to-count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cocopambag/ResNet
https://github.com/code-implementation1/Code1/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/code-implementation1/Code2/tree/main/AdaBin
https://github.com/code-implementation1/Code3/tree/main/FaceAttribute
https://github.com/code-implementation1/Code4/tree/main/HRNetW48_seg
https://github.com/code-implementation1/Code4/tree/main/IndexNet
https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir
https://github.com/code-implementation1/Code7/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/code-implementation1/Code7/tree/main/resnetv2
https://github.com/code-implementation1/Code9/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/coderwangson/Face-anti-spoofing-based-on-color-texture-analysis
https://github.com/coderwangson/Learn-Convolutional-Neural-Network-for-Face-Anti-Spoofing_pytorch
https://github.com/cornell-zhang/allo-pldi24-artifact
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/ArbitraryStyle
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/ArbitraryStyle-tfjs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cubiq/comfyui_ipadapter_plus
https://github.com/cv-stuttgart/springwebsite
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
https://github.com/cvlab-unibo/depth4tom-code
Learning Depth Estimation for Transparent and Mirror Surfaces
https://github.com/cxmomo/Neighbor-Vote
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting
https://github.com/cyb1212/Monocular-Depth-Estimation-vis-Transfer-learning-Winter-school-
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/daimonae/Fundus-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/daniallin/DenseDepth-Pytorch
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/danielamassiceti/CCA-visualdialogue
https://github.com/danielparada1/RL_SpaceInvaders
https://github.com/danielzgsilva/MonoDepthAttacks
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/danijorgesantos/detectcars
https://github.com/darrishabh/coviprox
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datawiz1984/MLND_Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datawiz1984/malaria-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datoboat/Vehicle-Detection
https://github.com/davyneven/fastSceneUnderstanding
https://github.com/dbbert/dfn
https://github.com/dead-mazai/avia-missing-airplanes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.