Zero-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

PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor The dataset can be used for image classification and object detection tasks. Image Source: Object Detection and Recognition in Images

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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.

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.