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prithivMLmods/Gym-Exercise-Video-Analysis

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.… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Gym-Exercise-Video-Analysis.

sourceHugging Faceapache-2.0updated 23d agoView on Hugging Face
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Dataset Card

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

FieldTypeDescription
videoVideoSource video file of the exercise execution
sliced_framesSequence[Image]List of sampled sequential video frames (typically 5 keyframes per clip)
frames_responsestringDetailed 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

python
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:

python
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.