| --- |
| 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](https://huggingface.co/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` |
|
|
| ```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**. |
