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README.md

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 ---
 license: apache-2.0
+library_name: hatchfinder
+pipeline_tag: image-segmentation
+tags:
+- hatchfinder
+- image-segmentation
+- blueprint
+- floorplan
+- hatching
+- pattern-matching
+- reference-image
+- architecture
+- construction
+- bim
+- pytorch
 ---
+
+# GreenMap/hatch-finder-3.5m
+
+HatchFinder is a lightweight reference-conditioned model for locating a given hatch pattern in architectural and construction drawings.
+
+The model takes a blueprint, a search mask defining the region of interest, and a reference image of the hatch pattern to find. It produces a dense pixel-wise heatmap indicating where the reference hatch is present.
+
+**GitHub:** [GreenMap-chan/hatch-finder](https://github.com/GreenMap-chan/hatch-finder)
+
+## Overview
+
+HatchFinder is designed for reference-based hatch detection rather than classification into a fixed set of hatch classes.
+
+- Finds a hatch pattern provided as a reference image.
+- Produces a dense pixel-wise probability heatmap.
+- Uses a search mask to restrict detection to selected regions of the drawing.
+- Does not require the target hatch to belong to a predefined class.
+- Designed for architectural plans, construction drawings, and similar technical documents.
+- Approximately 3.5M parameters.
+
+## Inputs
+
+The model takes three inputs:
+
+- **Drawing** — RGB image of the architectural or construction drawing.
+- **Search mask** — binary mask specifying the region in which the hatch should be searched for.
+- **Reference hatch** — RGB image containing an example of the hatch pattern to locate.
+
+## Output
+
+The model outputs a single-channel dense logits map with the same spatial resolution as the drawing.
+
+After applying sigmoid, each pixel represents the predicted probability that it belongs to the reference hatch pattern.
+
+```python
+heatmap = torch.sigmoid(logits)
+heatmap = heatmap * search_mask
+```
+
+## Architecture
+
+HatchFinder uses a custom multi-scale convolutional architecture consisting of:
+
+- Multi-scale drawing encoder.
+- Multi-scale reference hatch encoder.
+- Learned drawing-to-reference matching features.
+- Gated multi-scale matching.
+- FiLM-based reference conditioning.
+- U-Net-like heatmap decoder.
+
+The model is trained end-to-end for reference-conditioned pixel-level hatch detection.
+
+## Validation Metrics
+
+| Metric | Value |
+|--------|------:|
+| Validation loss | 0.05881 |
+| BCE loss | 0.01343 |
+| Dice loss | 0.05672 |
+| Dice coefficient | 0.94328 |
+
+The metrics above were measured on the validation split used for this model and should not be interpreted as performance on arbitrary blueprint datasets.
+
+## Training
+
+- Task: Reference-conditioned hatch segmentation
+- Framework: PyTorch
+- Parameters: ~3.5M
+- Loss: Masked BCEWithLogits + Dice loss
+- Training examples: 7,000
+- Input: RGB drawing + binary search mask + RGB reference hatch
+- Output: Pixel-wise hatch logits
+
+Training includes geometric and visual augmentations applied independently where appropriate to the drawing and reference hatch.
+
+## Installation
+
+```bash
+pip install hatchfinder
+```
+
+## Usage
+
+```python
+from hatchfinder import HatchFinder
+
+model = HatchFinder("model.pt")
+```
+
+See the hatch-finder [repository](https://github.com/GreenMap-chan/hatch-finder) for training, preprocessing, and inference examples.
+
+## License
+
+Apache License 2.0

config.json

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+{}

model.pt

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