Fine-Grained Image Classification
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 )
Leaderboards
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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.
SOTA for Image Classification on SVHN
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
ICML 2019
• lukemelas/EfficientNet-PyTorch
• 
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
SOTA for Image Classification on Stanford Cars (using extra training data)
FINE-GRAINED IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH TRANSFER LEARNING
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.
#2 best model for
Image Classification
on CIFAR-10
(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.
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.
#6 best model for
Fine-Grained Image Classification
on CUB-200-2011
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION
Fixing the train-test resolution discrepancy
NeurIPS 2019
• facebookresearch/FixRes
• 
Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86. 4% (top-5: 98. 0%) (single-crop).
SOTA for Image Classification on iNaturalist (using extra training data)
Destruction and Construction Learning for Fine-Grained Image Recognition
CVPR 2019
• JDAI-CV/DCL
•
In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge.
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
The resulting model significantly outperforms state-of-the-art models with similar accuracy in terms of mCE and inference throughput.
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED VISUAL RECOGNITION IMAGE RETRIEVAL TRANSFER LEARNING
Pairwise Confusion for Fine-Grained Visual Classification
ECCV 2018
• abhimanyudubey/confusion
•
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
#2 best model for
Fine-Grained Image Classification
on Stanford Dogs
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
CVPR 2018
• richardaecn/cvpr18-inaturalist-transfer
•
We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.
#2 best model for
Fine-Grained Image Classification
on CUB-200-2011
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED VISUAL CATEGORIZATION TRANSFER LEARNING