https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Depth Estimation
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- 평가표
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- 보존된 평가 기록
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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/sangHa0411/CIFAR100
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sanoyo/dl-model
https://github.com/sarthakbaiswar/Artistic-Styling
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sasha-kap/CV-to-Maps
Automatic Discovery and Geotagging of Objects from Street View Imagery
https://github.com/satyajitghana/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/say2sarwar/DeepAcid
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/scarlettjiali/Tools_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/scut-math-srp/Distance-measure-based-on-depth-estimation
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/seansoleyman/cifar10-resnet
https://github.com/seatizendoi/dinovdeau
https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN
https://github.com/sebsquire/FlowersRecognition_keras
https://github.com/seenu-g/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/seepala98/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/seishinkikuchi/test
https://github.com/sejong-rcv/2021.Paper.TransDSSL
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/selkerdawy/joint-pruning-monodepth
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter pruning
https://github.com/semi0612/cat_and_dog_vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/serre-lab/gala_tpu
https://github.com/seungryong/JCNF
https://github.com/seyrankhademi/ResNet_CIFAR10
https://github.com/sfu-gruvi-3dv/Self-Supervised-Human-Depth
Self-Supervised Human Depth Estimation from Monocular Videos
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shao-chi/ImageCaption
https://github.com/shaochenze/ear
https://github.com/shijianjian/efficientnet-pytorch-3d
https://github.com/shirgur/UnsupervisedDepthFromFocus
Single Image Depth Estimation Trained via Depth from Defocus Cues
https://github.com/shlomi-amitai/FSRE-Depth
https://github.com/shngjz/lighteddepth
https://github.com/shoaibahmed/CrossLayerPooling
https://github.com/shoji9x9/CIFAR-10-By-small-ResNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.