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-1/ms-code-217/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet50
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinynet
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/u2net
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/unisiam
https://github.com/2023-MindSpore-1/ms-code-22
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/LR-EGAN
eGAN: Unsupervised approach to class imbalance using transfer learning
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/ResNet
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/SSD
SSD: A Unified Framework for Self-Supervised Outlier Detection
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/Alexnet
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-1/ms-code-33
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-1/ms-code-34
https://github.com/2023-MindSpore-1/ms-code-37
https://github.com/2023-MindSpore-1/ms-code-38
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-1/ms-code-40
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/2023-MindSpore-1/ms-code-44
https://github.com/2023-MindSpore-1/ms-code-46
https://github.com/2023-MindSpore-1/ms-code-47
https://github.com/2023-MindSpore-1/ms-code-48
https://github.com/2023-MindSpore-1/ms-code-49
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CBAM
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/centernet_resnet101
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-1/ms-code-62
MultiGrain: a unified image embedding for classes and instances
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TNT
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/ViG
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/vit_base
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/wave_mlp
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/wideresnet
https://github.com/2023-MindSpore-1/ms-code-86
https://github.com/2023-MindSpore-4/Code-5/tree/main/HireMLP
https://github.com/2023-MindSpore-4/Code-5/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.