Few-Shot Object Detection

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

LVIS is a dataset for long tail instance segmentation. It has annotations for over 1000 object categories in 164k images. Source: LVIS

Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories. Source: FSOD

The ELEVATER benchmark is a collection of resources for training, evaluating, and analyzing language-image models on image classification and object detection. ELEVATER consists of: - Benchmark: A benchmark suite that consists of 20 image classification datasets and 35 object detection datasets, augmented with external knowledge - Toolkit: An automatic hyper-parameter tuning toolkit; Strong language-augmented efficient model adaptation methods. - Baseline: Pre-trained language-free and language-augmented visual models. - Knowledge: A platform to study the benefit of external knowledge for vision problems. - Evaluation Metrics: Sample-efficiency (zero-, few-, and full-shot) and Parameter-efficiency. - Leaderboard: A public leaderboard to track performance on the benchmark The ultimate goal of ELEVATER is to drive research in the development of language-image models to tackle core computer vision problems in the wild.

DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia. It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Source: https://github.com/alzayats/DeepFish Image Source: https://github.com/alzayats/DeepFish

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

A large-scale logo image database for logo detection and brand recognition from real-world product images. Source: LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including currency recognition. To aid with this task, we present BankNote-Net, an open dataset for assistive currency recognition. The dataset consists of a total of 24,816 embeddings of banknote images captured in a variety of assistive scenarios, spanning 17 currencies and 112 denominations. These compliant embeddings were learned using supervised contrastive learning and a MobileNetV2 architecture, and they can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft, which has over a 100 thousand monthly active users.

CAMO-FS Dataset comes with the paper entitled The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation. DOI: https://doi.org/10.1109/ACCESS.2024.3432873 Official GitHub: https://github.com/danhntd/FS-CDIS The dataset includes camouflaged images and the annotations under COCO JSON format.

VizWiz-FewShot is a a few-shot localization dataset originating from photographers who authentically were trying to learn about the visual content in the images they took. It includes nearly 10,000 segmentations of 100 categories in over 4,500 images that were taken by people with visual impairments.