Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/fairseq/tree/main/examples/data2vec
공식 코드https://github.com/facebookresearch/forwardgnn
공식 코드https://github.com/facebookresearch/hcrbounds
공식 코드Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
https://github.com/facebookresearch/hiera
공식 코드Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/facebookresearch/irmae
공식 코드https://github.com/facebookresearch/low-shot-shrink-hallucinate
공식 코드Low-shot Visual Recognition by Shrinking and Hallucinating Features
https://github.com/facebookresearch/low-shot-with-diffusion
공식 코드https://github.com/facebookresearch/mae
공식 코드https://github.com/facebookresearch/maws
공식 코드The effectiveness of MAE pre-pretraining for billion-scale pretraining
https://github.com/facebookresearch/mega
공식 코드https://github.com/facebookresearch/meru
공식 코드https://github.com/facebookresearch/metaclip
공식 코드https://github.com/facebookresearch/mixup-cifar10
공식 코드https://github.com/facebookresearch/mobile-vision
공식 코드FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
https://github.com/facebookresearch/moco
공식 코드Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/facebookresearch/moco-v3
공식 코드An Empirical Study of Training Self-Supervised Vision Transformers
https://github.com/facebookresearch/msn
공식 코드https://github.com/facebookresearch/multigrain
공식 코드MultiGrain: a unified image embedding for classes and instances
https://github.com/facebookresearch/mvit
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/noisy_few_shot
공식 코드https://github.com/facebookresearch/omnivore
공식 코드https://github.com/facebookresearch/pycls
공식 코드https://github.com/facebookresearch/qmnist
공식 코드https://github.com/facebookresearch/simsiam
공식 코드https://github.com/facebookresearch/sscd-copy-detection
공식 코드https://github.com/facebookresearch/suncet
공식 코드https://github.com/facebookresearch/swav
공식 코드Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
https://github.com/facebookresearch/tan
공식 코드https://github.com/facebookresearch/tome
공식 코드https://github.com/facebookresearch/unbiased-teacher
공식 코드https://github.com/facebookresearch/vicreg
공식 코드VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
https://github.com/facebookresearch/vissl
공식 코드https://github.com/facebookresearch/vissl/blob/main/MODEL_ZOO.md
공식 코드ClusterFit: Improving Generalization of Visual Representations
https://github.com/facebookresearch/xcit
공식 코드https://github.com/facico/goat-peft
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.