Publish streetlevel-poles dataset

README.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+  - object-detection
+language:
+  - en
+pretty_name: Street-level Poles & Towers
+size_categories:
+  - 1K<n<10K
+tags:
+  - object-detection
+  - yolo
+  - streetlevel
+  - panoramax
+  - openstreetmap
+  - poles
+  - towers
+  - infrastructure
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train/*.parquet
+      - split: validation
+        path: data/val/*.parquet
+      - split: test
+        path: data/test/*.parquet
+---
+
+# Street-level Poles & Towers
+
+Object detection dataset of utility **poles** and **towers** in street-level
+imagery, packaged from the *Object Detection (Berkeley)* working set produced
+under the HOT-OSM YOLO experiments. The dataset is shipped in two compatible
+layouts:
+
+- **HuggingFace parquet** (default loader): images embedded as bytes, with
+  COCO-style bounding boxes (`x, y, w, h` in pixels), class labels, and
+  per-image source metadata. Loadable with `datasets.load_dataset(...)`.
+- **COCO JSON** (`coco/instances_{train,val,test}.json`) alongside the
+  original **YOLO** layout description in `data.yaml`, for native use with
+  Ultralytics or any COCO-aware trainer.
+
+## Classes
+
+| id | name  |
+|----|-------|
+| 0  | pole  |
+| 1  | tower |
+
+## Splits
+
+| split | images | positive | negative | boxes | pole boxes | tower boxes |
+|-------|-------:|---------:|---------:|------:|-----------:|------------:|
+| train | 2,001  | 227      | 1,774    | 338   | 199        | 139         |
+| val   | 996    | 501      | 495      | 816   | 505        | 311         |
+| test  | 503    | 256      | 247      | 409   | 273        | 136         |
+| total | 3,500  | 984      | 2,516    | 1,563 | 977        | 586         |
+
+Note: the train split is heavily dominated by **negative** (no-object)
+samples and the positive/negative ratios are not balanced across splits.
+This reflects the upstream curation rather than a sampling decision; users
+training detectors may want to resample for class balance.
+
+## Geographic coverage
+
+Per-image geographic metadata (latitude, longitude, Panoramax id, source
+URL) is available on **1,051 negative samples** sourced from
+[panoramax.openstreetmap.fr](https://panoramax.openstreetmap.fr). Coverage
+spans roughly Western Europe and East Asia (latitude 24.8 to 53.1,
+longitude -4.6 to 121.5). Positive samples generally do not carry
+per-image coordinates: their geographic origin is encoded only as a
+filename prefix (`bryan_*`, `shanghai_*`, `oakland_*`, `berkeley_*`) or
+through Roboflow-style identifiers.
+
+Visualizations available under `artifacts/`:
+
+- `geo_map.html` — interactive map of all geotagged samples.
+- `geo_map.png` — static scatter plot of latitude vs longitude.
+- `split_class_counts.png` — bounding box counts by class and split.
+- `image_counts.png` — image counts by split, broken down positive / negative.
+- `boxes_per_image.png` — histogram of boxes per positive image.
+- `source_distribution.png` — image counts by source prefix.
+
+## Source provenance
+
+| source prefix | images |
+|---------------|-------:|
+| bryan         | 3,452  |
+| roboflow      |    42  |
+| shanghai      |     5  |
+| oakland       |     1  |
+
+`bryan_*` is the dominant prefix and includes both positive and Panoramax
+sourced negative samples. `roboflow` denotes images stored under the
+Roboflow-style identifier scheme (`<hash>_jpg.rf.<token>.jpg`).
+
+## Parquet schema
+
+```python
+{
+  "image":       Image(decode=True),                          # JPEG bytes
+  "image_id":    Value("string"),                             # filename stem
+  "width":       Value("int32"),
+  "height":      Value("int32"),
+  "objects": {
+    "bbox":      Sequence(Sequence(Value("float32"), length=4)),   # [x, y, w, h] in pixels (COCO)
+    "category":  Sequence(ClassLabel(names=["pole", "tower"])),
+  },
+  "source":         Value("string"),    # bryan | roboflow | shanghai | oakland
+  "panoramax_id":   Value("string"),    # populated for geotagged negatives
+  "source_url":     Value("string"),    # original Panoramax derivate URL
+  "lat":            Value("float64"),
+  "lon":            Value("float64"),
+}
+```
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("kshitijrajsharma/streetlevel-poles")
+ds["train"][0]["image"]            # PIL.Image
+ds["train"][0]["objects"]          # {"bbox": [...], "category": [...]}
+```
+
+For the YOLO / Ultralytics workflow, point at the `data.yaml` shipped with
+the dataset after downloading the `coco/` and image folders, or regenerate
+the YOLO layout from the parquet shards (helper in this repo).
+
+## Baseline
+
+A short YOLOv11n baseline was trained on this dataset for 10 epochs at
+`imgsz=640`. Test split results:
+
+| metric          | value |
+|-----------------|------:|
+| mAP@50          | 0.332 |
+| mAP@50-95       | 0.120 |
+| precision       | 0.455 |
+| recall          | 0.345 |
+
+See `artifacts/baseline_results.png`,
+`artifacts/baseline_confusion_matrix.png`, and
+`artifacts/baseline_pr_curve.png` for full curves.
+
+This is intended as a sanity-check baseline, not a state-of-the-art result.
+The dataset is small, class-imbalanced, and the train split is dominated by
+negatives, so longer training and rebalancing are likely to help.
+
+## Reproducing
+
+```bash
+just setup
+just extract
+just convert
+just viz
+just parquet
+just train       # YOLOv11n, 10 epochs
+just upload
+```
+
+## License
+
+Released under **CC-BY-SA-4.0**. The negative samples derive from
+Panoramax / OpenStreetMap-sourced street-level imagery; share-alike
+compatibility is preserved.
+
+## Citation
+
+If you use this dataset, please cite the originating work and this
+distribution:
+
+```
+@misc{streetlevel-poles,
+  title  = {Street-level Poles & Towers},
+  author = {Sharma, Kshitij Raj and contributors},
+  year   = {2026},
+  howpublished = {HuggingFace Datasets},
+  url    = {https://huggingface.co/datasets/kshitijrajsharma/streetlevel-poles}
+}
+```

artifacts/baseline_metrics.json

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+{
+  "model": "yolo11n.pt",
+  "epochs": 10,
+  "imgsz": 640,
+  "save_dir": "/home/krschap/code/hf/streetlevel-poles/runs/yolo11n-baseline",
+  "metrics": {
+    "test_map50": 0.33220510652939983,
+    "test_map50_95": 0.11985106815782695,
+    "test_precision": 0.45535131929129263,
+    "test_recall": 0.3453592975651799
+  }
+}

data.yaml

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+names:
+- pole
+- tower
+nc: 2
+test: /content/drive/MyDrive/ObjectDetection/YOLO_Dataset/images/test
+train: /content/drive/MyDrive/ObjectDetection/YOLO_Dataset/images/train
+val: /content/drive/MyDrive/ObjectDetection/YOLO_Dataset/images/val

data/classes.txt

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+pole
+tower

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