Omkar2118/datasets huggingface dataset:
Extreme Countix-AV

* 214 videos under various extreme sight conditions for audiovisual repetition counting

* 7 vision challenges: camera viewpoint changes, cluttered background, low illumination, fast motion, disappearing activity, scale variation, low resolution

Introducing paper: Repetitive Activity Counting by Sight and Sound
UFPR-ADMR-v1

This dataset contains 2,000 dial meter images obtained on-site by employees of the Energy Company of Paraná (Copel), which serves more than 4 million consuming units in the Brazilian state of Paraná. The images were acquired with many different cameras and are available in the JPG format with 320×640 or 640×320 pixels (depending on the camera orientation). 

The dataset is split into three sets: training (1200 images), validation (400 images), and testing (400 images). Every image has the following annotations available in a .txt file: the counter’s corners (x1, y1), (x2, y2), (x3, y3), (x4, y4). The corners can be used to rectify the counter patch and represent, respectively, the top-left, top-right, bottom-right, and bottom-left corners. For each dial, the current position (x, y, w, h) and the corresponding reading (pointed values and final reading). All counters of the dataset (regardless of meter type) have 4 or 5 dials; thus, 9,097 dials were manually annotated.

Source: [Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines](/paper/deep-learning-for-image-based-automatic-dial)

Introducing paper: Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines