Fine-Grained Image Classification

71 papers with code • 32 benchmarks • 26 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 )

Greatest papers with code

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

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.

Breast Tumour Classification Domain Generalization +8

AutoAugment: Learning Augmentation Policies from Data

tensorflow/models 24 May 2018

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

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

huggingface/transformers ICLR 2021

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 Image Classification on Tiny ImageNet Classification (using extra training data)

Document Image Classification Fine-Grained Image Classification

ResMLP: Feedforward networks for image classification with data-efficient training

rwightman/pytorch-image-models 7 May 2021

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.

Ranked #5 on Image Classification on ImageNet V2 (using extra training data)

Data Augmentation Fine-Grained Image Classification +3

TResNet: High Performance GPU-Dedicated Architecture

rwightman/pytorch-image-models 30 Mar 2020

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 #6 on Fine-Grained Image Classification on Oxford 102 Flowers (using extra training data)

Fine-Grained Image Classification General Classification +2

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

rwightman/pytorch-image-models ICML 2019

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 +1

Transformer in Transformer

rwightman/pytorch-image-models 27 Feb 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).

Fine-Grained Image Classification

Training data-efficient image transformers & distillation through attention

lucidrains/vit-pytorch 23 Dec 2020

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

Fine-Grained Image Classification

When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations

google-research/vision_transformer 3 Jun 2021

Vision Transformers (ViTs) and MLPs signal further efforts on replacing hand-wired features or inductive biases with general-purpose neural architectures.

 Ranked #1 on Domain Generalization on ImageNet-R (Top 1 Accuracy metric)

Domain Generalization Fine-Grained Image Classification +1

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

tensorflow/lingvo 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.

Ranked #4 on Fine-Grained Image Classification on Birdsnap (using extra training data)

Fine-Grained Image Classification Machine Translation