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configs:
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data_files:
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path: grounding_cross_coord_to_coord/train-*
- config_name: grounding_cross_letter_to_letter
data_files:
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path: grounding_cross_letter_to_letter/train-*
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path: grounding_task_a_icl_outside/train-*
- config_name: grounding_task_a_icl_within
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path: grounding_task_a_icl_within/train-*
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path: grounding_task_a_zero_shot/train-*
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path: grounding_task_b_icl_outside/train-*
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path: grounding_task_b_icl_within/train-*
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data_files:
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path: grounding_task_b_zero_shot/train-*
- config_name: task1_image
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path: task1_image/train-*
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data_files:
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path: task1_three_view/train-*
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data_files:
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path: task2_three_view/train-*
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data_files:
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path: task3_image/train-*
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data_files:
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path: task3_missing_part_image/train-*
- config_name: task3_missing_part_three_view
data_files:
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path: task3_missing_part_three_view/train-*
- config_name: task3_three_view
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="20" style="vertical-align:middle; margin-right:10px;" /> -->
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
</h1>
</div>
<p align="center">
🌐 <a href="https://ai4manufacturing.github.io/forge-web/">Website</a> | 📑 <a href="https://arxiv.org/abs/2604.07413">Paper</a> | 💻 <a href="https://github.com/AI4Manufacturing/FORGE">Code</a> | 🤗 <a href="https://huggingface.co/datasets/AI4Manufacturing/forge">Dataset</a>
</p>
<div align="center">
<img src="pipeline.png" width="100%" alt="FORGE Pipeline Overview">
</div>
## 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
@misc{jian2026forge,
title={FORGE:Fine-grained Multimodal Evaluation for Manufacturing Scenarios},
author={Xiangru Jian and Hao Xu and Wei Pang and Xinjian Zhao and Chengyu Tao and Qixin Zhang and Xikun Zhang and Chao Zhang and Guanzhi Deng and Alex Xue and Juan Du and Tianshu Yu and Garth Tarr and Linqi Song and Qiuzhuang Sun and Dacheng Tao},
year={2026},
eprint={2604.07413},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2604.07413},
}
``` |