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README.md

@@ -26,6 +26,7 @@ Instruction-prompted object detection with vision-language models via vLLM. Desi
 |---|---|---|
 | `qwen3vl-detect.py` | **Stable** | Standard zero-shot detection |
 | `qwen3vl-detect-fewshot.py` | **Experimental** | One-shot in-context example — pass an example image + its labelled output to demonstrate the task |
+| `inspect-detections.py` | **Utility** | Render a side-by-side PNG of model detections vs ground truth (when source has GT) for each row of an output dataset. Used to generate all visual comparisons in this README. |
 
 The script name says `qwen3vl` because the bbox-output convention (`bbox_2d` JSON normalised to 0-1000) is the Qwen-VL family standard, but the script is mostly model-agnostic — you can pass any vLLM-supported VLM via `--model` and adjust the prompt accordingly.
 
@@ -77,9 +78,46 @@ Return a JSON array.
 
 Empirical lessons:
 
+- **Enumerate the class set in the prompt** when you have one. Listing the 23 ENA24 species or the 5 CPPE-5 PPE classes explicitly in the prompt constrains the model's label vocabulary and cuts class drift (e.g. "Squirrel" vs "Eastern Gray Squirrel").
 - **Keep prompts positive-only.** Telling the model what to *find* works better than "ignore X" clauses, which can trigger over-conservative empty outputs on busy pages.
 - **Give the model a broad taxonomy and filter the output post-hoc** rather than narrowing the prompt. Asking for 7 categories then `label in {"Photograph", "Illustration"}` produces better detections on the categories you care about than asking for just those 2 classes upfront.
-- **Sub-labels are descriptive prose**, not constrained. The model uses them for colour ("blue nitrile"), material ("white N95 respirator"), reference designators ("R246", "U201"), or transcribed text (actual headline text on a newspaper page).
+- **Sub-labels are descriptive prose**, not constrained. The model uses them for colour ("blue nitrile"), material ("white N95 respirator"), behaviour/posture, reference designators ("R246", "U201"), or transcribed text (actual headline text on a newspaper page).
+
+### Example prompts used in the worked examples
+
+The exact prompt strings that produced the example datasets below — copy and adapt:
+
+**Wildlife (ENA24, 23 species):**
+```
+Detect every distinct animal, person, or vehicle visible in this camera trap photo.
+For each, return a JSON object with "bbox_2d": [x1, y1, x2, y2] (coordinates normalised
+to 0-1000), "label" (one of: Bird, Eastern Gray Squirrel, Eastern Chipmunk, Woodchuck,
+Wild Turkey, White_Tailed_Deer, Virginia Opossum, Eastern Cottontail, Human, Vehicle,
+Striped Skunk, Red Fox, Eastern Fox Squirrel, Northern Raccoon, Grey Fox, Horse, Dog,
+American Crow, Chicken, Domestic Cat, Coyote, Bobcat, American Black Bear), and
+"sub_label" (a short description, e.g. behaviour, posture, or partial-visibility note).
+Return a JSON array.
+```
+
+**Medical PPE (CPPE-5, 5 classes):**
+```
+Detect every piece of medical personal protective equipment (PPE) visible in this
+image. For each, return a JSON object with "bbox_2d": [x1, y1, x2, y2] (coordinates
+normalised to 0-1000), "label" (exactly one of: Coverall, Face_Shield, Gloves,
+Goggles, Mask), and "sub_label" (a short colour or material descriptor). Return a
+JSON array. Each object must be a distinct visible item; do not return overlapping
+or duplicate boxes.
+```
+
+**Historical newspapers (Beyond Words, 7 classes):**
+```
+Detect every distinct visual element on this historical newspaper page. For each,
+return a JSON object with "bbox_2d": [x1, y1, x2, y2] (coordinates normalised to
+0-1000), "label" (one of: Photograph, Illustration, Map, Comics/Cartoon, Editorial
+Cartoon, Headline, Advertisement), and "sub_label" (a short description of the
+content). Return a JSON array. Each object must be a distinct visible region; do
+not return overlapping or duplicate boxes.
+```
 
 ## Hardware notes
 
@@ -113,6 +151,23 @@ These public datasets were generated with the recommended config (Qwen3.6-35B-A3
 | `davanstrien/qwen3.6-cppe5-20samples` | `rishitdagli/cppe-5` (medical PPE photos, 5 classes) | Detect Coverall/Face_Shield/Gloves/Goggles/Mask | Labelling-grade boxes (within ~10 px of GT on most items). Descriptive sub-labels ("white N95 respirator", "blue nitrile") |
 | `davanstrien/qwen3.6-beyond-words-20samples` | `biglam/loc_beyond_words` (WW1-era newspapers, 7 classes) | Detect Photograph/Illustration/Map/Comics/Editorial Cartoon/Headline/Advertisement | Mixed — clean per-element detection on most pages, occasional empty output on the densest layouts. Notable: `sub_label` often contains transcribed headline text |
 
+## Inspecting outputs
+
+`inspect-detections.py` renders side-by-side PNGs of model detections vs ground truth (when available) so you can eyeball quality before trusting the dataset. Runs locally (no GPU needed) — installs matplotlib + datasets via uv on first invocation.
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/vlm-object-detection/raw/main/inspect-detections.py \
+    davanstrien/qwen3.6-ena24-20samples \
+    --split train \
+    --source davanstrien/ena24-detection \
+    --source-split train \
+    --max-rows 20
+```
+
+The `--source` flag is optional but recommended when the source dataset has bbox ground truth — the script will read the source's `objects.category` ClassLabel feature to recover human-readable class names (which get stripped from the output dataset's features on `push_to_hub` round-trip). Without `--source`, only the model detections panel is rendered.
+
+PNGs are written to `/tmp/<output-slug>-viz/` by default (override with `--out-dir`). Each file is `row{i}_id{image_id}.png`. Per-row text summary (detection counts, label histogram, bbox value range) is printed to stdout.
+
 ## Known limitations
 
 - **Bbox precision varies by model and image.** On natural-object photos (PPE, products, animals), boxes are often within 10 px of ground truth. On dense document layouts, boxes are looser and occasionally model gives up on a page entirely.