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/alibaba/EasyNLP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/alibaba/easyrobust/tree/main/examples/imageclassification/imagenet/drvit
Discrete Representations Strengthen Vision Transformer Robustness
https://github.com/alibaba/esim-response-selection
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/alibaba/megatron-llama
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://github.com/alibabasglab/frcrn
https://github.com/aliengirlliv/teachable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/alihassanijr/DISCERN
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/aliiafkari/SUT_Dataset
https://github.com/alililia/ascend_FaceAttribute
https://github.com/alililia/ascend_ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/alililia/ascend_SE-Net
https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_contrib/tree/main/ascend_xception
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/alililia/ms_contrib/tree/main/gpu_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_contrib/tree/main/gpu_xception
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/alililia/ms_extend/tree/main/ascend_senet
https://github.com/alililia/ms_extend/tree/main/gpu_efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/alililia/ms_extend/tree/main/gpu_resnet
https://github.com/alililia/u2net_Ascend
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/alililia/u2net_GPU
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/alililia/vit_base_Ascend
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alililia/vit_base_GPU
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alililia/wideresnet_Ascend
https://github.com/alililia/wideresnet_GPU
https://github.com/alinstein/X_RAY
https://github.com/alisha17/benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alisterta/Persian-text-to-speech
https://github.com/allen108108/Model-Optimizer_Implementation
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/allenai/dnw
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/allenai/learning-curve
https://github.com/allenhaozhu/ease
Manifold Mixup: Better Representations by Interpolating Hidden States
https://github.com/alondj/Pytorch-Gpipe
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/alphadl/darts.pytorch1.1
https://github.com/alphadl/lookahead.pytorch
https://github.com/alrhub/arturo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.