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CREMI dataset
The CREMI dataset consists of brain electron microscopy images (EM), and the ultimate goal is to reconstruct neurons at the micro-scale level. -
COCO-Stuff 164K
Semantic segmentation is one of the most fundamental tasks that aims to classify every pixel of a given image into a specific class. It is widely applied to many applications... -
MoNuSeg dataset
The MoNuSeg dataset is published for the Multi-organ Nuclei Segmentation challenge in MICCAI 2018. The training dataset consists of 30 images generated from multiple organs... -
Image dataset
The dataset used in the paper is a set of images, and the authors used it to train and test their ladder network model. -
Internal Dataset
The internal dataset contains 6 million real-world driving scenarios from Las Vegas (LV), Seattle (SEA), San Francisco (SF), and the campus of the Stanford Linear Accelerator... -
Segmentation dataset for jet flames
The dataset used for training and testing the UNet and Attention UNet models for segmentation of radiation zones within the jet flames. -
Caltech-UCSD Birds-200-2011 Dataset
The Caltech-UCSD Birds-200-2011 Dataset consists of 11,169 bird images from 200 categories and each category has 60 images averagely. -
SA-1B dataset
The SA-1B dataset used for training the SAM model, containing 11M images. -
COCONut-B [6] and EntitySeg [49] and DIS5K [51] datasets
The dataset used for training the RWKV-SAM model, containing 242K images, including COCO labeled and unlabelled images, EntitySeg dataset with 30k high-resolution images... -
BraTS and MVTec AD datasets
The dataset used in the paper is a combination of medical images, including T1, T2, and Flair MRI scans from BraTS, and images from MVTec AD. -
Pascal VOC, Pascal Context, COCO-Object, Cityscapes, and ADE20k datasets
The Pascal VOC, Pascal Context, COCO-Object, Cityscapes, and ADE20k datasets are used for evaluation of the proposed method. -
EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM
EdgeSAM is an accelerated variant of the Segment Anything Model (SAM) optimized for efficient execution on edge devices with minimal compromise in performance. -
Tiny Anomalies
A novel benchmark for evaluating methods on the original, high-resolution image and ground-truth masks, focusing on segmentation performance as a function of the size of anomalies. -
Fission Gas Bubble Segmentation on U-10Zr Annular Fuels
The dataset is used for fission gas bubble segmentation on U-10Zr annular fuels. -
MICCAI 2018 segmentation decathlon challenge
MRI dataset for hippocampus segmentation -
ADE20K Dataset
The ADE20K dataset is a large-scale dataset for semantic segmentation. It contains 20,000 images with 150 semantic categories, with 20,000 images for training, 2,000 images for... -
Middlebury dataset
Guided depth super-resolution (GDSR) involves restor- ing missing depth details using the high-resolution RGB image of the same scene. -
Penobscot 3D Survey
The Penobscot 3D Survey dataset is a real seismic dataset acquired offshore Nova Scotia, Canada. It consists of a horizontal stack of 2D seismic images (slices), creating a 3D...
