https://github.com/Abhijeet2396/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
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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.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Fixup Initialization: Residual Learning Without Normalization
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural Networks for Fashion Image Classification and Visual Search
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization
Very Deep Convolutional Networks for Large-Scale Image Recognition
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Deep Neural Network Compression for Image Classification and Object Detection
Momentum Contrast for Unsupervised Visual Representation Learning
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.