Update README.md

README.md

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 configs:
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   data_files:
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   data_files:
   - split: train
     path: task3_three_view/train-*
+license: mit
+task_categories:
+- question-answering
+- image-text-to-text
+- visual-question-answering
+language:
+- en
+tags:
+- Manufacturing
+- 3D
+- Industry
+- Engineering
+pretty_name: Forge
+size_categories:
+- 1K<n<10K
 ---
+
+
+<div align="center">
+  <h1>
+    <img src="forge_icon.png" alt="FORGE Logo" height="50" style="vertical-align:middle; margin-right:10px;" />
+    FORGE: A Benchmark for Manufacturing Anomaly Detection with VLMs
+  </h1>
+</div>
+
+<p align="center">
+&nbsp;&nbsp;🌐&nbsp;<a href="https://ai4manufacturing.github.io/forge-web/">Website</a>&nbsp;&nbsp; | &nbsp;&nbsp;📑&nbsp;<a href="">Paper</a>&nbsp;&nbsp; | &nbsp;&nbsp;💻&nbsp;<a href="https://github.com/AI4Manufacturing/FORGE">Code</a>&nbsp;&nbsp; | &nbsp;&nbsp;🤗&nbsp;<a href="https://huggingface.co/datasets/AI4Manufacturing/forge">Dataset</a>&nbsp;&nbsp;
+</p>
+
+<div align="center">
+  <img src="pipeline.png" width="100%" alt="FORGE Pipeline Overview">
+</div>
+
+## Overview
+
+**FORGE** evaluates Vision-Language Models on industrial manufacturing anomaly detection. It covers three core tasks across photo and three-view rendered modalities, plus spatial grounding ablations. All images are embedded -- no external files needed.
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AI4Manufacturing/forge", "task1_three_view", split="train")
+print(ds[0].keys())
+ds[0]["test_image"]  # PIL Image
+```
+
+## Configs
+
+### Core Tasks
+
+| Config | Cases | Task | Modality |
+|--------|------:|------|----------|
+| `task1_image` | 451 | Wrong model detection (MCQ) | Photo |
+| `task1_three_view` | 496 | Wrong model detection (letter) | Three-View |
+| `task2_three_view` | 830 | Anomaly classification (normal + defect type) | Three-View |
+| `task3_image` | 857 | Extra/wrong part detection (MCQ) | Photo |
+| `task3_three_view` | 309 | Extra/wrong part detection (letter) | Three-View |
+| `task3_missing_part_image` | 240 | Missing part identification (MCQ) | Photo |
+| `task3_missing_part_three_view` | 137 | Missing part identification (MCQ) | Three-View |
+
+### Grounding Ablation (Single-Image)
+
+| Config | Cases | Description |
+|--------|------:|-------------|
+| `grounding_task_a_zero_shot` | 500 | Coord → Letter, zero-shot |
+| `grounding_task_a_icl_within` | 500 | Coord → Letter, ICL (same image) |
+| `grounding_task_a_icl_outside` | 500 | Coord → Letter, ICL (cross image) |
+| `grounding_task_b_zero_shot` | 500 | Letter → Coord, zero-shot |
+| `grounding_task_b_icl_within` | 500 | Letter → Coord, ICL (same image) |
+| `grounding_task_b_icl_outside` | 500 | Letter → Coord, ICL (cross image) |
+
+### Grounding Ablation (Cross-Image)
+
+| Config | Cases | Description |
+|--------|------:|-------------|
+| `grounding_cross_letter_to_letter` | 513 | Match parts by letter across images |
+| `grounding_cross_coord_to_coord` | 513 | Match parts by coordinate across images |
+
+**Total: 6,846 cases across 15 configs**
+
+## Data Fields
+
+Each row is self-contained with all images embedded. Unused image slots hold a 1x1 placeholder. Use `n_normal_refs` / `n_icl_examples` to know how many are real.
+
+**Task 1/3 Image** -- `test_image`, `grounding_image`, `assembly_name`, `assembly_description`, `error_case`, `ref_image_0..4`, `icl_ori_image_0..2`, `icl_grounding_image_0..2`, `n_normal_refs`, `n_icl_examples`
+
+**Task 1/3 Three-View** -- `test_image`, `gt_parts` (JSON), `query_description`, `scenario_name`, `error_case`, `ref_image_0..4`, `icl_image_0..2`, `icl_gt_letters` (JSON), `n_normal_refs`, `n_icl_examples`
+
+**Task 2 Three-View** -- `test_image`, `defect_type`, `is_normal`, `component_type`, `component_description`, `ref_image_0..4`, `icl_image_0..2`, `icl_metadata` (JSON), `n_normal_refs`, `n_icl_examples`
+
+**Missing Part** -- `test_image`, `assembly_name`, `assembly_description`, `choices_text`, `gt_letter`, `gt_answer`, `mcq_mapping` (JSON), `ref_image_0..4`, `icl_image_0..2`, `icl_gt_letters` (JSON), `n_normal_refs`, `n_icl_examples`
+
+**Grounding (single)** -- `test_image`, `target_coord` (JSON), `target_letter`, `choices` (JSON), `gt_choice_letter`, `icl_image_0..2`, `icl_metadata` (JSON), `n_icl_examples`
+
+**Grounding (cross)** -- `ref_image`, `test_image`, `ref_hint`, `ref_hint_coord` (JSON), `test_choices` (JSON), `test_mcq_options` (JSON), `gt_answer`
+
+## Evaluation Code
+
+See the [FORGE GitHub repo](https://github.com/AI4Manufacturing/FORGE) for the full evaluation toolkit supporting OpenRouter, OpenAI, Anthropic, Google, and vLLM backends.
+
+## Citation
+
+```bibtex
+@article{jianforge2026,
+  title={FORGE: A Benchmark for Manufacturing Anomaly Detection with VLMs},
+  author={Jian, Xiangru and Xu, Hao and Pang, Wei and Zhao, Xinjian and Tao, Chengyu and Zhang, Qixin and Zhang, Xikun and Zhang, Chao and Deng, Guanzhi and Xue, Alex and Du, Juan and Yu, Tianshu and Tarr, Garth and Sun, Qiuzhuang and Tao, Dacheng},
+  year={2026}
+}
+```