Omkar2118/datasets huggingface dataset:
Penguin dataset

The penguin dataset is a collection of images of penguin colonies in Antarctica coming from the larger penguin watch project, which was setup with the purpose of monitoring their changes in population. The images are taken by fixed cameras in over 40 different locations, which have been capturing an image per hour for several years. In order to track the colony sizes, the number of penguins in each of the images in the dataset is required.
So far, the penguin count has been done with the help of citizen scientists on the Penguin Watch site by Zooniverse, where interested users can place dots on top of the penguins. Here we release part of this data to the vision community in order to learn from the crowd-sourced dot-annotations to automatically annotate these images.

For more information about the project, please visit Penguin Watch.
NCT-CRC-HE-100K

The NCT-CRC-HE-100K dataset is a set of 100,000 non-overlapping image patches extracted from 86 H$\&$E stained human cancer tissue slides and normal tissue from the NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). While the dataset Colorectal Cacner-Validation-Histology-7K (CRC-VAL-HE-7K) consist of 7180 images extracted from 50 patients with colorectal adenocarcinoma and were used to create a dataset that does not overlap with patients in the NCT-CRC-HE-100K dataset. It was created by pathologists by manually delineating tissue regions in whole slide images into the following nine tissue classes: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).

Image source: [https://www.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf](https://www.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)
flower_photos

A large set of images of flowers

Homepage: https://www.tensorflow.org/tutorials/load_data/images

Dataset size: 221.83 MiB
UHRSD
Full name: Ultra High-Resolution Saliency Detection Dataset

Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at resolving this contradiction, we propose a novel one-stage framework called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to extract features from different resolution images independently and then graft the features from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the largest dataset in both quantity and resolution for high-resolution SOD task, which can be used for training and testing in future research. Sufficient experiments on UHRSD and widely-used SOD datasets demonstrate that our method achieves superior performance compared to the state-of-the-art methods.

Introducing paper: Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
DAVIS-S

To enrich the diversity, we also collect 92 images which are suitable for saliency detection from DAVIS [27], a densely annotated high-resolution video segmentation dataset. Im- ages in this dataset are precisely annotated and have very high resolutions (i.e.,1920!1080). We ignore the categories of the objects and generate saliency ground truth masks for this dataset. For convenience, the collected dataset is denot- ed as DAVIS-S.

Introducing paper: Towards High-Resolution Salient Object Detection