Datasets:

prithivMLmods
/

Gym-Exercise-Video-Analysis

license: apache-2.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: video
      dtype: video
    - name: frames_response
      dtype: string
    - name: sliced_frames
      list: image
  splits:
    - name: train
      num_bytes: 1928709371
      num_examples: 500
  download_size: 1928580946
  dataset_size: 1928709371
task_categories:
  - video-text-to-text
language:
  - en
tags:
  - Gym-Exercise
  - Video-Analysis
pretty_name: ' Gym-Exercise'
size_categories:
  - n<1K

Gym-Exercise-Video-Analysis

Gym-Exercise-Video-Analysis is a specialized multimodal video understanding dataset comprising 500 annotated gym workout and exercise clips. It is designed for fine-tuning and evaluating Video-Language Models (Video-LLMs), visual fitness coaches, and temporal exercise analysis systems. Each entry pairs exercise videos and extracted frame sequences with in-depth textual descriptions, biomechanical observations, form evaluations, and routine tracking.

Curator: prithivMLmods
Total Samples: 500 rows
Total Size: ~1.93 GB
Format: Parquet (video, sliced_frames, frames_response)
Modalities: Image, Video, Text
Split: Train (500 rows)

Dataset Structure & Schema

Each record contains raw video data, sampled/sliced temporal frames, and comprehensive step-by-step descriptive analysis.

Feature Fields

Field	Type	Description
`video`	`Video`	Source video file of the exercise execution
`sliced_frames`	`Sequence[Image]`	List of sampled sequential video frames (typically 5 keyframes per clip)
`frames_response`	`string`	Detailed analysis describing the exercise movement, technique, body posture, and equipment used

Example Analysis Text

"The video captures an individual performing a seated workout routine... As you monitor your fitness routine, I can clearly see that the movement maintains steady tempo, targeted engagement of the upper body muscles, and controlled eccentric extension."

How to Use

Loading with `datasets`

from datasets import load_dataset

# Load dataset
dataset = load_dataset("prithivMLmods/Gym-Exercise-Video-Analysis", split="train")

# Access a single record
sample = dataset[0]
sliced_frames = sample["sliced_frames"]  # List of PIL Images
analysis = sample["frames_response"]     # Text analysis

print("Analysis preview:", analysis[:200])
print(f"Extracted keyframes: {len(sliced_frames)}")

Video-LLM Fine-Tuning Format Example

Convert records into multi-image or video prompt conversations for models like Qwen2-VL, Video-LLaVA, or LLaVA-OneVision:

def format_for_video_llm(example):
    return {
        "images": example["sliced_frames"],
        "prompt": "Analyze this gym exercise sequence. Identify the movement, assess form, and describe the physical execution in detail.",
        "response": example["frames_response"]
    }

formatted_sample = format_for_video_llm(dataset[0])

Intended Uses

Video-LLM Alignment: Instruction tuning multimodal models on multi-frame sequential reasoning and dense video captioning.
AI Fitness & Coaching Assistants: Training automated gym form-checkers, exercise counters, and workout logging models.
Action & Movement Recognition: Temporal motion understanding across diverse gym environments, lighting conditions, and workout equipment.

License

This dataset is distributed under the Apache-2.0 License.