Fine-Grained Image Classification

180 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 )

Most implemented papers

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

google-research/vision_transformer 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

tensorflow/tpu 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.

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.

Training data-efficient image transformers & distillation through attention

facebookresearch/deit 23 Dec 2020

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

DINOv2: Learning Robust Visual Features without Supervision

facebookresearch/dinov2 14 Apr 2023

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.

Sharpness-Aware Minimization for Efficiently Improving Generalization

google-research/sam ICLR 2021

In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.

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

facebookresearch/deit NeurIPS 2021

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

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.

ResNet strikes back: An improved training procedure in timm

rwightman/pytorch-image-models 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.

Learning to Navigate for Fine-grained Classification

yangze0930/NTS-Net 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.