Fine-Grained Image Classification
153 papers with code • 35 benchmarks • 33 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 )
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
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.
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
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.
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.