Fine-Grained Image Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

36 leaderboard tables shown for this task, 36 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 36 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

41 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 41 until expanded.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 200 papers with code (353 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
  28 May 2019 144 repositories listed Syntology ran 171 of 302 samples · 131 unverified · 112 pointer-only (licence)
  Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
• Training data-efficient image transformers & distillation through attention
  23 Dec 2020 40 repositories listed Syntology ran 12 of 19 samples · 7 unverified · 3 pointer-only (licence)
  In this work, we produce a competitive convolution-free transformer by training on Imagenet only.
• AutoAugment: Learning Augmentation Policies from Data
  24 May 2018 33 repositories listed Syntology ran 6 of 43 samples · 37 unverified · 2 pointer-only (licence)
  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.
• DINOv2: Learning Robust Visual Features without Supervision
  14 Apr 2023 26 repositories listed Syntology ran 21 of 46 samples · 25 unverified · 12 pointer-only (licence)
  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.
• ResMLP: Feedforward networks for image classification with data-efficient training
  7 May 2021 19 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
• Sharpness-Aware Minimization for Efficiently Improving Generalization
  3 Oct 2020 18 repositories listed Syntology ran 8 of 20 samples · 12 unverified · 6 pointer-only (licence)
  In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.
• ResNet strikes back: An improved training procedure in timm
  1 Oct 2021 14 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  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.
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
  16 Nov 2018 13 repositories listed Syntology ran 1 of 25 samples · 24 unverified · 16 pointer-only (licence)
  Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
• Transformer in Transformer
  27 Feb 2021 12 repositories listed Syntology ran 16 of 24 samples · 8 unverified · 5 pointer-only (licence)
  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…
• Learning to Navigate for Fine-grained Classification
  2 Sep 2018 12 repositories listed Syntology ran 3 of 9 samples · 6 unverified
  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…
• Escaping the Big Data Paradigm with Compact Transformers
  12 Apr 2021 9 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 1 pointer-only (licence)
  Our models are flexible in terms of model size, and can have as little as 0.
• Big Transfer (BiT): General Visual Representation Learning
  24 Dec 2019 9 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  We conduct detailed analysis of the main components that lead to high transfer performance.
• Three things everyone should know about Vision Transformers
  18 Mar 2022 8 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  (2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to a higher resolution and to other classification tasks.
• Gradient Centralization: A New Optimization Technique for Deep Neural Networks
  3 Apr 2020 8 repositories listed Syntology ran 2 of 29 samples · 27 unverified · 1 pointer-only (licence)
  It has been shown that using the first and second order statistics (e.
• Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
  26 Jan 2017 8 repositories listed
  We verify the proposed method on a practical problem: person re-identification (re-ID).
• Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
  20 Mar 2020 6 repositories listed
  Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales.
• ImageNet-21K Pretraining for the Masses
  22 Apr 2021 5 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks.
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
  11 Feb 2021 5 repositories listed Syntology ran 8 of 10 samples · 2 unverified · 9 pointer-only (licence)
  In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset.
• Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
  8 Mar 2020 5 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
• Your Diffusion Model is Secretly a Zero-Shot Classifier
  28 Mar 2023 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.
• With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
  29 Apr 2021 4 repositories listed Syntology ran 4 of 5 samples · 1 unverified
  On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.
• Presence-Only Geographical Priors for Fine-Grained Image Classification
  12 Jun 2019 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories.
• See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
  26 Jan 2019 4 repositories listed
  Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
• Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
  4 Dec 2017 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks.
• Bilinear CNNs for Fine-grained Visual Recognition
  29 Apr 2015 4 repositories listed
  We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also…
• DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
  28 Mar 2024 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures.
• SpinalNet: Deep Neural Network with Gradual Input
  7 Jul 2020 3 repositories listed
  Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
• Proxy Anchor Loss for Deep Metric Learning
  31 Mar 2020 3 repositories listed
  The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity.
• TResNet: High Performance GPU-Dedicated Architecture
  30 Mar 2020 3 repositories listed
  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.
• The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
  11 Feb 2020 3 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.

Syntology lines on 23 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.