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/MishaLaskin/vqvae
https://github.com/MiuGod0126/ConvS2S_Paddle
https://github.com/MiuGod0126/Mlp-Mixer-Paddle
https://github.com/MiuGod0126/RAM-Paddle
https://github.com/MiuGod0126/Stochastic-Depth-Paddle
https://github.com/Mixture-of-Rookie/PASSL
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
https://github.com/Mjrovai/covid19Xray
Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset
https://github.com/Mnpr/OC-DeepLearning
https://github.com/Mnpr/Thoractic-Diseases-Detection
https://github.com/Mnpr/Thoractic_Disease_Detection
https://github.com/MohamedFarag21/DL-papers-implementation/tree/main/Computer_vision/CBAM:%20Convolutional%20Block%20Attention%20Module/Code
https://github.com/Mohammad-Rahmdel/FGSM-Tensorflow2-Caltech101
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
https://github.com/MoritzTaylor/maml-rl-tf2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MorningStarOvO/dissect
Understanding the Role of Individual Units in a Deep Neural Network
https://github.com/Morxrc/kaggle_generate-dogs_public-1st-private-16th-solution
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/MrD1360/Keras-implementation-of-MobileNetv3-with-L-RASSP
https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MrWonderson/WideResNet
https://github.com/MukhlasAdib/KITTI_Mapping
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/Mukundaram/Mukund
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Mungosin/Mozgalo
https://github.com/Murali81/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Murali81/InfoGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MustafaMustafa/cosmoGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MustafaMustafa/dcgan-tf-benchmark
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Myyyr/transseg2d
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/NASA-NeMO-Net/NeMO-Net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/NEUdeep/Swin-Transformer-Object-Detection
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/NVIDIA/DeepLearningExamples
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/NVIDIA/retinanet-examples
https://github.com/NVlabs/A-ViT
https://github.com/NVlabs/DG-Net
https://github.com/NVlabs/NViT
Global Vision Transformer Pruning with Hessian-Aware Saliency
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.