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/conceptofmind/DeepViT-flax
https://github.com/conceptofmind/LeViT-flax
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
https://github.com/conceptofmind/NesT-flax
https://github.com/conceptofmind/Parallel-ViT-flax
https://github.com/conceptofmind/PiT-flax
https://github.com/conceptofmind/Region-ViT-flax
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/conceptofmind/Simple-ViT-flax
https://github.com/conceptofmind/Token-to-Token-ViT-flax
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
https://github.com/conceptofmind/Twins-SVT-Flax
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
https://github.com/conceptofmind/ViT-Small-Datasets-flax
https://github.com/conceptofmind/ViT-haiku
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/conormdurkan/neural-statistician
https://github.com/contactnipun/Computer-Vision
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/cooijmanstim/recurrent-batch-normalization
https://github.com/cornell-zhang/allo-pldi24-artifact
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/correr-zhou/spml-acktheunknown
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/cove-adml/adml-anon
https://github.com/cpark321/bayesian-neural-networks
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/cpark321/uncertainty-deep-learning
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/cpn/ShuffledVideoSorter
https://github.com/cpuimage/MaxDropout
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values
https://github.com/cranial-xix/famo
https://github.com/crashmoon/MAML-Pytorch-Multi-GPUs
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/craston/object_detection_cib
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
https://github.com/cravingoxygen/neuralstat
https://github.com/crisostomi/mass
https://github.com/criteo-research/pytorch-ada
https://github.com/croros/STAR_Network_Pytorch
https://github.com/crowdAI/crowdai-musical-genre-recognition-starter-kit
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/cryu854/ArbitraryStyle
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/ArbitraryStyle-tfjs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cs-giung/course-dl-TP
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cs-mshah/Adapter-Bert
https://github.com/cs20m072/DL-assignment-2-Part-B
Improving neural networks by preventing co-adaptation of feature detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.