Yoga for all: A Comprehensive Collection of Yoga Images and Videos dataset
Bibliographic record
Abstract
The dataset comprises both images and videos depicting right and wrong postures for a variety of Yoga asanas. The focus of the dataset is on 10 specific Yoga postures, namely Anantasana, Ardhakati Chakrasana, Bhujangasana, Kati Chakrasana, Marjariasana, Parvatasana, Sarvangasana, Tadasana, Vajrasana, and Viparita Karani. The Image dataset comprises a total of 11,344 images and is organized into 10 subfolders, each corresponding to a specific Yoga asana. Within each subfolder, there are two additional folders labeled "Right Steps" and "Wrong Steps". The "Right Steps" folder contains several subfolders, each representing a specific step in the right sequence of the Yoga asana and displaying the corresponding images. On the other hand, the "Wrong Steps" folder includes multiple subfolders, each showing images of an wrong steps in the sequence of the Yoga asana. The Yoga asana video dataset consists of 8 videos for each posture, comprising 4 videos demonstrating the right posture from 4 different angles and 4 videos exhibiting the wrong posture from 4 different angles. This dataset includes a total of 80 videos for 10 Yoga asanas, with 40 videos demonstrating the right postures captured from 4 different angles, and 40 videos illustrating the wrong postures from 4 different angles. The dataset has advantages for various groups, such as app developers, machine learning researchers, Yoga instructors, and Yoga practitioners. Machine learning researchers can utilize this dataset to train computer vision algorithms in recognizing and categorizing various yoga postures automatically. App developers can use the dataset to generate yoga apps that present users with visual guidance on executing each posture and keeping track of their progress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".