Fine-Grained Image Classification
174 papers with code • 35 benchmarks • 36 datasets
Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
( Image credit: Looking for the Devil in the Details )
Benchmarks
These leaderboards are used to track progress in Fine-Grained Image Classification
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Libraries
Use these libraries to find Fine-Grained Image Classification models and implementationsDatasets
MNIST
CUB-200-2011
Oxford 102 Flower
STL-10
Stanford Cars
Food-101
Caltech-101
iNaturalist
FGVC-Aircraft
EMNIST
Most implemented papers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
google-research/vision_transformer
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ICLR 2021
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
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.
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
facebookresearch/deit
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NeurIPS 2021
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Sharpness-Aware Minimization for Efficiently Improving Generalization
google-research/sam
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ICLR 2021
In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
tensorflow/lingvo
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NeurIPS 2019
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
Learning to Navigate for Fine-grained Classification
yangze0930/NTS-Net
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ECCV 2018
In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher.
Transformer in Transformer
huawei-noah/CV-Backbones
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NeurIPS 2021
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).
ResNet strikes back: An improved training procedure in timm
rwightman/pytorch-image-models
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NeurIPS Workshop ImageNet_PPF 2021
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.
