Update README.md

README.md

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+# 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**.