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/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2_FPNlite
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet50
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code8/tree/main/Alexnet
https://github.com/2023-MindSpore-4/Code8/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2023-MindSpore-4/Code8/tree/main/CBAM
https://github.com/2023-MindSpore-4/Code8/tree/main/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/2023-MindSpore-4/Code8/tree/main/autoaugment
https://github.com/2023-MindSpore-4/Code8/tree/main/cait
https://github.com/2023-MindSpore-4/Code8/tree/main/convnext
https://github.com/2023-MindSpore-4/Code9/tree/main/Inception
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-4/Code9/tree/main/MobileNet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-4/Code9/tree/main/OCRNet
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code9/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-4/Code9/tree/main/SwinTransformer
https://github.com/2023-MindSpore-4/Code9/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/beit
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bit
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/convnext
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cvt
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deit
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/dinov2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/fnet
https://github.com/2024-MindSpore-1/Code3/tree/main/MaxViT
https://github.com/2024-MindSpore-1/Code3/tree/main/VI-LayoutXLM
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/2024-MindSpore-1/Code3/tree/main/gMLP
https://github.com/2024-MindSpore-1/Code3/tree/main/replknet
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
https://github.com/2024-MindSpore-1/Code4/tree/main/Inception
https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv1
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.