https://github.com/alililia/ms_extend/tree/main/gpu_retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alinlab/LfF
Learning from Failure: Training Debiased Classifier from Biased Classifier
https://github.com/aliprf/Ad-Corre
https://github.com/alisha17/benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alldbi/Factorized-Spatial-Embeddings
Unsupervised learning of object landmarks by factorized spatial embeddings
https://github.com/allenai/satlas-super-resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/alrojo/lasagne_residual_network
https://github.com/alsgkals2/CoReD_Released
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
https://github.com/alsgkals2/cored
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
https://github.com/altndrr/persona
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection
https://github.com/alvarobartt/serving-pytorch-models
https://github.com/alvinwan/shiftresnet-cifar
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
https://github.com/amamov/vgg16-vs-resnet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aman-arya/Any-Body-Can-Dance
https://github.com/aman-garg0001/fruit-classifier
https://github.com/amanshenoy/pose-invariant-face-recognition
https://github.com/amazon-research/long-short-term-transformer
https://github.com/amazon-science/long-short-term-transformer
https://github.com/ambityga/memsem
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amdegroot/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aminekha/AI-For-2022
https://github.com/amirhfarzaneh/dacl
https://github.com/amittiwary42/target-detection-eeg-signals
https://github.com/amogh7joshi/engagement-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/fer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/media-vision
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/amogh7joshi/plant-health-detection
https://github.com/anax32/dimensionality-reduction
https://github.com/andersoncarlosfs/image_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/andreas128/NTIRE21_Learning_SR_Space
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
https://github.com/andreasveit/densenet-pytorch
https://github.com/andrijdavid/clinical-brain-interface-2020
https://github.com/andrire/RNNASIP-pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/angelvillar96/FaceEmoji
https://github.com/anguyen8/sam
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.