Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/bogireddytejareddy/3d-squeezenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/bojianhou/oagd
https://github.com/bojone/keras_lookahead
https://github.com/bojone/r-drop
https://github.com/bojone/rnn
https://github.com/bojone/tiger
https://github.com/boluoweifenda/CNN
https://github.com/bond005/yandex-shifts-weather
https://github.com/bonlime/keras-deeplab-v3-plus
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/booker-max/unsupervised-deraining-with-event-camera
https://github.com/borealisai/perturbed-forgetting
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/boris127/vehicle-detection
https://github.com/borisdayma/clip-jax
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bourcierj/rdfia-tme10-gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/bowenbaker/metaqnn
Designing Neural Network Architectures using Reinforcement Learning
https://github.com/bowensong0312/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/boyellow/adaad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/bozliu/E2E-Keyword-Spotting
https://github.com/bpuladi/Automated-PD-L1-Scoring
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/brain-bzh/MCNN
https://github.com/brain-bzh/pefsl
https://github.com/braincreators/octconv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/brainimageanalysis/marmosetgeneatlas_adult
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/brandonrobertz/AcademicUrlTitles
https://github.com/brandontrabucco/im2txt_express
https://github.com/brandontrabucco/im2txt_match
https://github.com/branislavhesko/segmentation_framework
https://github.com/breadboykid/ResnetAgePrediciton
https://github.com/breakaway7/p3dc-shot
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
https://github.com/brianjychan/landuse
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/brightmart/roberta_zh
https://github.com/brohrer/sharpened-cosine-similarity
https://github.com/brown-palm/ObjectPrompt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bruno-sartori/imageGenerator
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/bruthyu/bpt-vlm
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.