About

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 )

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Subtasks

Datasets

Greatest papers with code

AutoAugment: Learning Augmentation Policies from Data

24 May 2018tensorflow/models

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.

FINE-GRAINED IMAGE CLASSIFICATION IMAGE AUGMENTATION

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION OBJECT DETECTION PEDESTRIAN ATTRIBUTE RECOGNITION PEDESTRIAN TRAJECTORY PREDICTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

Transformer in Transformer

27 Feb 2021rwightman/pytorch-image-models

By stacking the TNT blocks, we build the TNT model for image recognition.

FINE-GRAINED IMAGE CLASSIFICATION

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

ICLR 2021 rwightman/pytorch-image-models

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

 Ranked #1 on Fine-Grained Image Classification on Oxford-IIIT Pets (using extra training data)

DOCUMENT IMAGE CLASSIFICATION FINE-GRAINED IMAGE CLASSIFICATION

TResNet: High Performance GPU-Dedicated Architecture

30 Mar 2020rwightman/pytorch-image-models

In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.

 Ranked #1 on Fine-Grained Image Classification on Stanford Cars (using extra training data)

FINE-GRAINED IMAGE CLASSIFICATION MULTI-LABEL CLASSIFICATION OBJECT DETECTION

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

ICML 2019 rwightman/pytorch-image-models

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.

Ranked #2 on Fine-Grained Image Classification on Birdsnap (using extra training data)

FINE-GRAINED IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH TRANSFER LEARNING

Training data-efficient image transformers & distillation through attention

23 Dec 2020lucidrains/vit-pytorch

In this work, we produce a competitive convolution-free transformer by training on Imagenet only.

FINE-GRAINED IMAGE CLASSIFICATION

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

NeurIPS 2019 tensorflow/lingvo

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.

Ranked #3 on Fine-Grained Image Classification on Birdsnap (using extra training data)

FINE-GRAINED IMAGE CLASSIFICATION MACHINE TRANSLATION

Learning to Navigate for Fine-grained Classification

ECCV 2018 osmr/imgclsmob

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.

FINE-GRAINED IMAGE CLASSIFICATION

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

CVPR 2018 osmr/imgclsmob

Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks.

FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION