https://github.com/Baichenjia/Tensorflow-TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
A Simple Framework for Contrastive Learning of Visual Representations
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Very Deep Convolutional Networks for Large-Scale Image Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Unsupervised Representation Learning by Predicting Image Rotations
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.