Omkar2118/datasets huggingface dataset:
VTAB
Full name: Visual Task Adaptation Benchmark

The **Visual Task Adaptation Benchmark (VTAB)** is a benchmark designed to evaluate general visual representations². It consists of a diverse and challenging suite of tasks². The benchmark defines a good general visual representation as one that yields good performance on unseen tasks, when trained on limited task-specific data².

The VTAB benchmark contains the following 19 tasks that are derived from public datasets¹:
- Caltech101
- CIFAR-100
- CLEVR distance prediction
- CLEVR counting
- Diabetic Rethinopathy
- Dmlab Frames
- dSprites orientation prediction
- dSprites location prediction
- Describable Textures Dataset (DTD)
- EuroSAT
- KITTI distance prediction
- 102 Category Flower Dataset
- Oxford IIIT Pet dataset
- PatchCamelyon
- Resisc45
- Small NORB azimuth prediction
- Small NORB elevation prediction
- SUN397
- SVHN

The given model is independently fine-tuned for solving each of the above tasks¹. Average accuracy across all tasks is used to measure the model's performance¹. Detailed description of all tasks, evaluation protocol, and other details can be found in the VTAB paper¹.

(1) Visual Task Adaptation Benchmark. https://google-research.github.io/task_adaptation/.
(2) GitHub - google-research/task_adaptation. https://github.com/google-research/task_adaptation.
(3) GitHub - KMnP/vpt: ️ Visual Prompt Tuning [ECCV 2022] https://arxiv .... https://github.com/KMnP/vpt.

Introducing paper: The Visual Task Adaptation Benchmark
EchoNet-Dynamic Cardiac Ultrasound

EchoNet-Dynamic is a dataset of over 10k echocardiogram, or cardiac ultrasound, videos from unique patients at Stanford University Medical Center. Each apical-4-chamber video is accompanied by an estimated ejection fraction, end-systolic volume, end-diastolic volume, and tracings of the left ventricle performed by an advanced cardiac sonographer and reviewed by an imaging cardiologist.