345 datasets found

The dataset used in the paper is YOLOX-Nano dataset.

The dataset used in the paper is YOLOF dataset.

The dataset used in the paper is ImageNet, MS COCO, and Pascal VOC datasets.

The proposed model uses the Broad Bioimage Benchmark Collection (BBBC) dataset to detect objects in images.

The MSCOCO dataset is a large-scale image captioning dataset, containing 113,287 images with 5,000 validation images and 5,000 test images. The dataset is used for training and...

ProgS-RCNN: Progressive End-to-End Object Detection in Crowded Scenes

Crowded object detection via copy-paste

CrowdHuman is a challenging benchmark to evaluate the ability of crowded scene detection of detectors, which contains about 15k training images and 4k images for evaluation.

Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based...

The VGG-16 dataset is a large collection of images of objects.

Aerial imagery [2], derived from various sources such as drones, satellites, and high-altitude platforms, plays a pivotal role in numerous real-world applications. One of the...

The COCO test-dev dataset is used for instance segmentation. It contains 20k test-dev images.

ILSVRC is a large-scale image dataset containing over 1.2 million images across 1,000 classes.

PASCAL VOC 2007 dataset is a widely used dataset for object detection and semantic segmentation. We use all the split sets (training, validation, testing) in the VOC2007 dataset...

The UBnormal dataset is a recent and large dataset proposed for supervised anomaly detection. In contrast to other benchmarks, it is a synthetic dataset created with Cinema4D....

The Avenue data set consists of 16 training videos with a total 15328 frames and 21 test videos with a total of 15324.

The Objects365 dataset is a large-scale object detection dataset containing 365,000 images with 365 categories.

SODA10M contains 10M un-labeled images and 20K labeled images of size 1920 × 1080 with default format of JPEG.

The CODA dataset is a real-world road corner case dataset for object detection in autonomous driving.

The LabelMe dataset is a natural scene dataset used for testing the performance of the IBTM model on image classification tasks.