https://github.com/Mind23-2/MindCode-47
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Video Classification with Channel-Separated Convolutional Networks
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
A Simple Framework for Contrastive Learning of Visual Representations
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
Fixup Initialization: Residual Learning Without Normalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.