Card: usage, scoring and per-source table

README.md

@@ -36,7 +36,7 @@ configs:
   <a href="https://github.com/PeterAMassih/SQuID"><img src="https://img.shields.io/badge/GitHub-code-181717.svg?logo=github"></a>
 </p>
 
-SQuID asks vision-language models to measure satellite images: count objects, compute areas and reason about distances. Every answer is computed from pixel-level masks.
+SQuID asks vision-language models to measure satellite images: count objects, compute areas and reason about distances. Most benchmark answers are computed from reference masks; the 50 NAIP questions use human-consensus answers.
 
 ![Examples](https://huggingface.co/datasets/PeterAM4/SQuID/resolve/main/assets/examples.jpg)
 
@@ -46,12 +46,22 @@ SQuID asks vision-language models to measure satellite images: count objects, co
 from datasets import load_dataset
 
 bench = load_dataset("PeterAM4/SQuID", "benchmark", split="test")
-train = load_dataset("PeterAM4/SQuID", "train", split="train")
+ex = bench[0]
+ex["image"], ex["mask"]        # PIL images (mask is None for NAIP)
+ex["question"], ex["answer"]   # 'What percentage of the image is agricultural land within 50m of water bodies? (GSD: 0.3m)', '29.32'
 ```
 
-A numeric answer is correct inside `[acceptable_range_lower, acceptable_range_upper]`. Binary and `connected`/`fragmented` answers must match exactly.
+Score a prediction:
 
-Training rows point to image files. Download them, with all masks and raw JSON files:
+```python
+def is_correct(pred, ex):
+    lo, hi = ex["acceptable_range_lower"], ex["acceptable_range_upper"]
+    if lo is None:                       # yes/no (1/0) and connected/fragmented
+        return str(pred) == ex["answer"]
+    return lo <= float(pred) <= hi
+```
+
+The `train` config lists 75,687 questions whose `image` and `mask` are file paths in this repository. Download the files with:
 
 ```python
 from huggingface_hub import snapshot_download
@@ -60,17 +70,17 @@ snapshot_download("PeterAM4/SQuID", repo_type="dataset", local_dir="data")
 
 ## Data
 
-| Split | Questions | Images |
-|---|---:|---:|
-| `benchmark` | 2,000 | 639 |
-| `train` | 75,687 | 1,443 |
-
-- **Sources:** DeepGlobe (0.5 m), EarthVQA (0.3 m), solar panels (0.3 m), and 50 human-answered NAIP questions (1.0 m) in the benchmark. The two splits share no images.
-- **Tiers:** basic (713), spatial (610) and multi-step (677) questions, of 25 types.
-- **Tolerances** come from 500 human annotations: ±1.7 points for percentages and ±19% for counts.
-- **Masks:** EarthVQA answers use `_label2`, the human label with its background filled by a land-cover model (`_pred2`).
-
-v1.0, the January 2026 benchmark, is still available with `revision="v1.0"` ([DOI](https://doi.org/10.57967/hf/7565)).
+| Source | GSD | Benchmark | Train | Reference mask |
+|---|---|---:|---:|---|
+| DeepGlobe | 0.5 m | 549 | 35,542 | land-cover classes (`_label.png`) |
+| EarthVQA | 0.3 m | 1,304 | 36,190 | buildings and land cover (`_label2.png`) |
+| Solar panels | 0.3 m | 97 | 3,955 | panels (`_label.bmp`) |
+| NAIP | 1.0 m | 50 | — | none: median of 10 human annotators |
+| **Total** | | **2,000** | **75,687** | |
+
+- **Tiers:** basic (713), spatial (610) and multi-step (677) questions of 25 types. The two splits share no images.
+- **Tolerances:** numeric answers are accepted within the spread of 500 human annotations (±1.7 points for percentages, ±19% for counts).
+- **EarthVQA masks:** `_label2` is the human label (`_label`) with its unlabeled background filled by a land-cover model (`_pred2`). Answers are computed from `_label2`.
 
 ## License and citation