Update README.md

README.md

@@ -33,4 +33,96 @@ tags:
 - synthetic-captions
 - deep-caption
 - image
----
+---
+
+# MultiImage-Caption
+
+**MultiImage-Caption** is a multimodal dense captioning dataset featuring **10,602** entries designed for training, supervised fine-tuning (SFT), and evaluating multi-image Vision-Language Models (such as Qwen2-VL, LLaVA-NeXT, and PaliGemma). 
+
+Each sample pairs an interleaved set of multiple images (`images list`) with comprehensive, comparative, and granular descriptive captions (`response`) analyzing the context, subjects, and interactions across all provided images.
+
+- **Curator:** [prithivMLmods](https://huggingface.co/prithivMLmods)
+- **Total Samples:** 10,602 rows
+- **Total Size:** ~4.43 GB
+- **Format:** Parquet (`image`, `response`)
+- **Modalities:** Image, Text
+- **Split:** Train
+
+## Dataset Structure & Schema
+
+### Feature Fields
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `image` | `Sequence[Image]` | A list of multiple target RGB images grouped together per entry |
+| `response` | `string` | Dense, structured textual explanation breaking down and comparing each individual image |
+
+### Example Response Structure
+
+```text
+Here is a detailed explanation of each image:
+
+**Image 1**: This photograph captures an energetic moment at an outdoor event...
+**Image 2**: A contrasting perspective showing subjects interacting with their surroundings...
+```
+
+## How to Use
+
+### Loading with `datasets`
+
+```python
+from datasets import load_dataset
+
+# Load dataset from the Hugging Face Hub
+dataset = load_dataset("prithivMLmods/MultiImage-Caption", split="train")
+
+# Access a single sample
+sample = dataset[0]
+images = sample["image"]        # List of PIL Images
+response = sample["response"]   # Multi-image detailed caption
+
+print(f"Number of images in sample: {len(images)}")
+print("Response preview:\n", response[:250])
+```
+
+### Formatting for Multi-Image VLM SFT
+
+```python
+def format_multi_image_conversation(example):
+    num_images = len(example["image"])
+    image_tokens = "".join([f"<image_{i+1}>\n" for i in range(num_images)])
+    
+    prompt = (
+        f"{image_tokens}Provide a detailed, step-by-step description and comparative "
+        "analysis of each of the provided images."
+    )
+    
+    return {
+        "images": example["image"],
+        "prompt": prompt,
+        "completion": example["response"]
+    }
+
+```
+
+## Intended Uses
+
+* **Multi-Image Reasoning:** Training models to correlate, compare, and reason over sequences of visual inputs simultaneously.
+* **Dense Captioning:** Generating rich, descriptive long-form visual commentary instead of brief single-sentence captions.
+* **Interleaved Multimodal Instruction-Tuning:** Building datasets for conversational agents handling multi-image document analysis, video keyframes, or side-by-side visual comparisons.
+
+## License
+
+This dataset is distributed under the **Apache-2.0 License**.
+
+## Citation
+
+```bibtex
+@misc{prithivmlmods2026multiimagecaption,
+  title        = {MultiImage-Caption: A Dense Multi-Image Multimodal Dataset},
+  author       = {prithivMLmods},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/prithivMLmods/MultiImage-Caption}}
+}
+```