Fine-Grained Image Classification Edit Task
Computer Vision • Image Classification
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
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Subtasks
Datasets
Greatest papers with code
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.
Ranked #3 on
Image Classification
on SVHN
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.
Ranked #2 on
Pedestrian Attribute Recognition
on UAV-Human
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
• rwightman/pytorch-image-models
• 
By stacking the TNT blocks, we build the TNT model for image recognition.
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
• rwightman/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
In this work, we produce a competitive convolution-free transformer by training on Imagenet only.
Ranked #2 on
Image Classification
on iNaturalist 2018
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)
Learning to Navigate for Fine-grained Classification
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.
Ranked #16 on
Fine-Grained Image Classification
on Stanford Cars
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks.
Ranked #5 on
Fine-Grained Image Classification
on CUB-200-2011
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION