[Benchmark] Add DA-2K, ERQA, and RefSpatialBench support

[Qi-Zhangyang]

Description

This PR adds support for three new benchmarks to VLMEvalKit:

1. DA-2K

• Paper: DA-2K: A Challenging Benchmark for Relative Depth Estimation (NeurIPS 2024)
• Dataset: https://huggingface.co/datasets/depth-anything/DA-2K
• Features:
  • Relative depth estimation in VQA format
  • 1K images with 2K annotation pairs across 8 scene categories
  • Ground truth: which of two annotated points is closer to the camera
• Eval Metric: Accuracy

2. ERQA (Embodied Reasoning QA)

• Paper: Gemini Robotics: Bringing AI into the Physical World (2025)
• Dataset: https://huggingface.co/datasets/RunsenXu/ERQA
• Features:
  • VQA format with <image> placeholders
  • Supports multi-image questions
  • 400 samples covering spatial reasoning and world knowledge
  • 8 categories: Trajectory Reasoning, Action Reasoning, Pointing, State Estimation, Spatial Reasoning, Multi-view Reasoning, Task Reasoning, Other
• Eval Metric: Accuracy (overall + per-category)

3. RefSpatialBench

• Paper: RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics (NeurIPS 2025)
• Dataset: https://huggingface.co/datasets/BAAI/RefSpatial-Bench
• Features:
  • Point coordinate prediction tasks
  • Mask-based evaluation (Success Rate)
  • 3 splits: Location (100), Placement (100), Unseen (77)
  • Supports multiple output formats: JSON [(x, y)], Qwen3-VL [{"point_2d": [x, y]}], Gemini [{"point": [y, x]}], Molmo XML <points x1="50" y1="50"/>
• Eval Metric: Success Rate (predicted point falls within ground-truth mask)

Benchmark Results (Qwen3-VL-235B)

Benchmark Results

Benchmark	Ours (Qwen3-VL-235B-Instruct)	Reference
DA-2K	68.78	70.3 (Claude Opus 4.5, Seed 2.0 Report)
ERQA	48.5	43.0 (Qwen3-VL-235B-Instruct, Qwen3-VL Report)
RefSpatialBench	66.0	65.5 (Qwen3-VL-235B-Instruct, Qwen3-VL Report)

Note: RefSpatialBench results vary depending on the distance tolerance threshold used for evaluation. Our implementation supports both strict mask containment and centroid-distance-based evaluation. The table below shows the sensitivity to this threshold, where ≤ 0.07 aligns with the Qwen3-VL report.

Threshold	Score
Strict mask containment	50.5
≤ 0.05 (5% of image width)	59.0
≤ 0.07 (7% of image width)	66.0
≤ 0.10 (10% of image width)	74.0

Usage

# DA-2K
python run.py --data DA-2K --model gpt-4o

# ERQA
python run.py --data ERQA --model gpt-4o

# RefSpatialBench
python run.py --data RefSpatial-Location --model gpt-4o
python run.py --data RefSpatial-Placement --model gpt-4o
python run.py --data RefSpatial-Unseen --model gpt-4o

[FangXinyu-0913]

Hi @Qi-Zhangyang! Thanks for your contribution. Please help fix lint first.

[Qi-Zhangyang]

Finished