Fine-Grained Image Classification
118 papers with code • 31 benchmarks • 28 datasets
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 )
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
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.
It has been shown that using the first and second order statistics (e. g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance.