metadata
dataset_info:
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sequence: image
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- config_name: task1_three_view
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configs:
- config_name: grounding_cross_coord_to_coord
data_files:
- split: train
path: grounding_cross_coord_to_coord/train-*
- config_name: grounding_cross_letter_to_letter
data_files:
- split: train
path: grounding_cross_letter_to_letter/train-*
- config_name: grounding_task_a_icl_outside
data_files:
- split: train
path: grounding_task_a_icl_outside/train-*
- config_name: grounding_task_a_icl_within
data_files:
- split: train
path: grounding_task_a_icl_within/train-*
- config_name: grounding_task_a_zero_shot
data_files:
- split: train
path: grounding_task_a_zero_shot/train-*
- config_name: grounding_task_b_icl_outside
data_files:
- split: train
path: grounding_task_b_icl_outside/train-*
- config_name: grounding_task_b_icl_within
data_files:
- split: train
path: grounding_task_b_icl_within/train-*
- config_name: grounding_task_b_zero_shot
data_files:
- split: train
path: grounding_task_b_zero_shot/train-*
- config_name: task1_image
data_files:
- split: train
path: task1_image/train-*
- config_name: task1_three_view
data_files:
- split: train
path: task1_three_view/train-*
- config_name: task2_three_view
data_files:
- split: train
path: task2_three_view/train-*
- config_name: task3_image
data_files:
- split: train
path: task3_image/train-*
- config_name: task3_missing_part_image
data_files:
- split: train
path: task3_missing_part_image/train-*
- config_name: task3_missing_part_three_view
data_files:
- split: train
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
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
🌐 Website | 📑 Paper | 💻 Code | 🤗 Dataset
Quick Start
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 for the full evaluation toolkit supporting OpenRouter, OpenAI, Anthropic, Google, and vLLM backends.
Citation
@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},
}