Omkar2118/datasets huggingface dataset:
GroundCap

GroundCap is a novel grounded image captioning dataset derived from MovieNet, containing 52,350 movie frames with detailed grounded captions. The dataset uniquely features an ID-based system that maintains object identity throughout captions, enables tracking of object interactions, and grounds not only objects but also actions and locations in the scene.


### Data Instances

Each sample in the dataset contains:
- An image (movie frame)
- Object detections with:
 - Unique object IDs
 - Class labels
 - Confidence scores
 - Bounding box coordinates
- A grounded caption with three types of grounding tags:
 - `<gdo>` for grounding objects (e.g., "the person", "a car")
 - `<gda>` for grounding actions (e.g., "running", "sitting")
 - `<gdl>` for grounding locations (e.g., "on the bridge", "in the kitchen")

### Data Fields

- `id`: Unique identifier for each caption
- `image`: The movie frame being captioned
- `detections`: List of detected objects containing:
  - `id`: Object's unique identifier (integer starting at 0 for each class)
  - `label`: Object class label
  - `score`: Detection confidence score
  - `box`: Bounding box coordinates (x, y, w, h)
- `caption`: Grounded caption text with HTML tags
  - `<gdo>` tags ground object references to detections using `{class}-{id}` as attribute (e.g., `<gdo class="person" person-0>the man</gdo>`)
  - `<gda>` tags ground actions to objects using `{class}-{id}` as attribute (e.g., `<gda class="run" person-0>running</gda>`)
  - `<gdl>` tags ground locations to objects using `{class}-{id}` as attribute (e.g., `<gdl class="couch" couch-0>on the couch</gdl>`)
- `human_annotated`: Boolean indicating whether the caption was automatically generated (False) or human-refined (True)

Multiple objects can be referenced in a single tag. For instance, `<gdo class="person" person-0 person-1>the two people</gdo>` refers to two detected people,
namely to the detections with IDs 0 and 1 and the class label "person".

Introducing paper: GroundCap: A Visually Grounded Image Captioning Dataset
deepfake and real images

This dataset contains manipulated images and real images. The manipulated images are the faces which are created by various means. The source for this dataset was https://zenodo.org/record/5528418#.YpdlS2hBzDd
this dataset was processed as our will to get maximum outcome out of these images. Each image is a 256 X 256 jpg image of human face either real or fake
DL3DV-10k

DL3DV-10K is a dataset of real-world videos with scene annotations and camera parameters.
MFW+ (U-M)

MFW+ is a benchmark dataset for masked face recognition and an extended version of MFW. The original MFW, published as a benchmark for masked face recognition, is composed of 300 IDs and 3,000 images. However, with two duplicate IDs found in MFW, the dataset actually contains 298 unique IDs and 2,980 images. To evaluate models under various mask conditions and environments, we manually gathered additional data from the web. The refined and extended MFW, which we named MFW+, contains 606 IDs, 2,911 unmasked face images, and 2,838 masked face images.
Paper: https://bmvc2022.mpi-inf.mpg.de/0723.pdf

Introducing paper: PPL: Pairwise Prototype Learning for Masked Face Recognition
MFW+ (M-M)

MFW+ is a benchmark dataset for masked face recognition and an extended version of MFW. The original MFW, published as a benchmark for masked face recognition, is composed of 300 IDs and 3,000 images. However, with two duplicate IDs found in MFW, the dataset actually contains 298 unique IDs and 2,980 images. To evaluate models under various mask conditions and environments, we manually gathered additional data from the web. The refined and extended MFW, which we named MFW+, contains 606 IDs, 2,911 unmasked face images, and 2,838 masked face images. Paper: https://bmvc2022.mpi-inf.mpg.de/0723.pdf

Introducing paper: PPL: Pairwise Prototype Learning for Masked Face Recognition