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/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv3
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet
https://github.com/2024-MindSpore-1/Code4/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2024-MindSpore-1/Code4/tree/main/SwinTransformer
https://github.com/2024-MindSpore-1/Code5/tree/main/3D_DenseNet
DenseNet for Breast Tumor Classification in Mammographic Images
https://github.com/2024-MindSpore-1/Code5/tree/main/Alexnet
https://github.com/2024-MindSpore-1/Code5/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/2024-MindSpore-1/Code6/tree/main/3D_DenseNet
https://github.com/2024-MindSpore-1/Code6/tree/main/AVA_cifar
https://github.com/2024-MindSpore-1/Code6/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/2024-MindSpore-1/Code6/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2024-MindSpore-1/Code6/tree/main/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/2024-MindSpore-1/Code6/tree/main/autoaugment
https://github.com/2024-MindSpore-1/Code6/tree/main/beit
https://github.com/2024-MindSpore-1/Code7/tree/main/CBAM
https://github.com/2024-MindSpore-1/Code7/tree/main/cait
https://github.com/2024-MindSpore-1/Code7/tree/main/cct
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/2024-MindSpore-1/Code7/tree/main/convnext
https://github.com/2024-MindSpore-1/Code7/tree/main/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/21-projects-for-deep-learning/image2text
https://github.com/2han9x1a0release/RLCC
https://github.com/2manslkh/korean-food-classification-flask
https://github.com/2xic/notebooks
Understanding deep learning requires rethinking generalization
https://github.com/32shivang/Blind-Eye
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/3P2S/arcface
https://github.com/3dimaging/DeepLearningCamelyon
https://github.com/3dperceptionlab/visual-wetlandbirds
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
https://github.com/3outeille/DSD-TF2
https://github.com/3outeille/DSD-training
https://github.com/42Shawn/LLaVA-PruMerge
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
https://github.com/44825762/MyCapsNet
https://github.com/461054993/SDCN
Deep Clustering for Unsupervised Learning of Visual Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.