Fine-Grained Image Classification
118 papers with code • 31 benchmarks • 28 datasets
The Fine-Grained Image Classification task focuses on differentiating between hard-to-distinguish object classes, such as species of birds, flowers, or animals; and identifying the makes or models of vehicles.
( Image credit: Looking for the Devil in the Details )
Libraries
Use these libraries to find Fine-Grained Image Classification models and implementationsDatasets
Most implemented papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
AutoAugment: Learning Augmentation Policies from Data
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
Training data-efficient image transformers & distillation through attention
In this work, we produce a competitive convolution-free transformer by training on Imagenet only.
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Sharpness-Aware Minimization for Efficiently Improving Generalization
In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.
Transformer in Transformer
In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT).
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
ResNet strikes back: An improved training procedure in timm
We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work.
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
It has been shown that using the first and second order statistics (e. g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance.