https://github.com/aobaruwa/TensorFlow-Projects
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/aoru45/Deep-Anomaly-Detection-for-Generalized-Face-Anti-Spoofing
https://github.com/apllolulu/monodepth
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/arahosu/musicslots
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ariangc/breinchallenge
https://github.com/arjunarao619/SRCNN_Pytorch
https://github.com/asharakeh/probdet
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras
https://github.com/ashushekar/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ashutosh1919/FaceGenerationStyleGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/asprenger/keras_fc_densenet
https://github.com/astoff/dog-breed-app
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/astoycos/Mini_Project2
https://github.com/astra-vision/PaSCo
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/avinashpaul2012/neophyte
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/avocardio/resnet_vs_convnext
https://github.com/avuku06/G
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/ayushdabra/drone-images-semantic-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/lunar-landscape-images-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/retinal-oct-images-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/gan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/b8goal/PIRL-Residual-network
https://github.com/bailvwangzi/repulsion_loss_ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/barmayo/spatial_attention
https://github.com/bartoszpanek1/Real-time-emotion-recognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bayraktarbaris/SNGAN
The Devil is in the Decoder: Classification, Regression and GANs
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/ben-davidson-6/fixup
https://github.com/beneroth13/dinov2
https://github.com/bentrevett/pytorch-image-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bepierre/SpeechVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bertrandlalo/deep_dog_breeds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.