Omkar2118/datasets huggingface dataset:
Peripheral Blood Cell

The Peripheral Blood Cell} (PBC) dataset consists of 17,092 images. These images are further organized into the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (including promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes. Each image is 360 x 363 pixels in size and is in JPG format, annotated by expert clinical pathologists. This dataset focuses on images of peripheral blood cells. For our newly introduced dataset, we have selected five types of white blood cells from this dataset.
Blood cell detection dataset

T(BCD) dataset consisted of a total of 364 blood smear images with annotations. The dataset is labeled three groups: “RBC”, “WBC”, and “Platelets”. There are 364 images with 416 x 416 pixels resolution in the BCD dataset. This dataset was published on https://www.kaggle.com/datasets/adhoppin/blood-celldetection-datatset.
Raabin-wbc

(WBC) dataset which consisted of 14514 WBC images across five classes 301 basophils, 795 monocytes, 1066 eosinophils, 8891 neutrophils, and 3461 lymphocytes at resolutions of 575 x 575. The data set mainly focuses on the classification of white blood cells. We selected each type of white blood cells and introduced them into our new data set.
IS3 (Interactive-Synthetic Sound Source) Dataset

We introduce a new synthetic test set named IS3 for interactive sound source localization. By leveraging diffusion models, we generate images containing multiple sounding objects. Any
combination of sounding objects can appear in the same scene. Additionally, this dataset offers unusual scenes and unique combinations that are rarely found in nature, such as ‘a donkey playing a saxophone’ or ‘a sea lion on the snow’. This dataset provides both segmentation maps and bounding box information with class categories. IS3 includes 3240 images, resulting in 6480 unique audio-visual instances (with 2 objects per image) across 118 categories.
This dataset can be used in below tasks:
1) Sound Source Localization
2) Audio-Visual Segmentation
3) Semantic Segmentation

Introducing paper: Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment
MUSES: MUlti-SEnsor Semantic perception dataset
Full name: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty

MUSES offers 2500 multi-modal scenes, evenly distributed across various combinations of weather conditions (clear, fog, rain, and snow) and types of illumination (daytime, nighttime). Each image includes high-quality 2D pixel-level panoptic annotations and class-level and novel instance-level uncertainty annotations. Further, each adverse-condition image has a corresponding image of the same scene taken under clear-weather, daytime conditions. The annotation process for MUSES utilizes all available sensor data, allowing the annotators to also reliably label degraded image regions that are still discernible in other modalities. This results in better pixel coverage in the annotations and creates a more challenging evaluation setup.

The dataset provides public benchmarks for:

- Panoptic segmentation
- Uncertainty-aware panoptic segmentation
- Semantic segmentation
- Object detection

Sensor modalities included:

- Frame camera (RGB)
- MEMS lidar
- FMCW radar
- HD event camera
- IMU/GNSS sensor

Introducing paper: MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Vindr-Mammo

Large-scale benchmark dataset of full-field digital mammography, called VinDr-Mammo, which consists of 5,000 four-view exams with breast-level assessment and finding annotations. Each of these exams was independently double read, with discordance (if any) being resolved by arbitration by a third radiologist.