https://github.com/JunweiLiang/aicity_action
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
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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.
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Aggregated Residual Transformations for Deep Neural Networks
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
An Empirical Study of Training Self-Supervised Vision Transformers
Parameter-efficient Model Adaptation for Vision Transformers
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.