Create README.md

README.md

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+---
+datasets:
+- Riksarkivet/trolldomskommissionen_seg
+- Riksarkivet/svea_hovratt_seg
+- Riksarkivet/krigshovrattens_dombocker_seg
+- Riksarkivet/jonkopings_radhusratts_och_magistrat_seg
+- Riksarkivet/gota_hovratt_seg
+- Riksarkivet/frihetstidens_utskottshandlingar_seg
+- Riksarkivet/bergskollegium_relationer_och_skrivelser_seg
+- Riksarkivet/bergskollegium_advokatfiskalskontoret_seg
+tags:
+- instance segmentation
+- text regions
+- handwritten
+- htr
+---
+
+# Yolov9-textregions-handwritten
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+A yolov9 instance segmentation model for segmenting text-regions in handwritten running-text documents
+
+
+## Model Details
+
+### Model Description
+
+This model was developed for segmenting text-regions in handwritten running-text documents. It is meant to be implemented in an HTR-pipeline
+where one first segment text-regions, then text-lines within the regions, and then feed these text-lines to an HTR-model.
+
+
+
+- **Developed by:** The Swedish National Archives
+- **Model type:** yolov9
+- **License:** [More Information Needed]
+
+### Model Sources [optional]
+
+
+- **Repository:** [yolov9-regions-1](https://huggingface.co/Riksarkivet/yolov9-regions-1)
+- **Paper [optional]:** [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
+
+## Uses
+
+
+### Direct Use
+
+Segment text-regions in handwritten running-text documents
+
+### Downstream Use [optional]
+
+As part of an HTR-pipeline for transcribing entire pages of handwritten running-text documents. See [Swedish Lion Libre](https://huggingface.co/Riksarkivet/trocr-base-handwritten-hist-swe-2)
+for example usage with the [HTRflow package](https://github.com/AI-Riksarkivet/htrflow)
+
+
+## How to Get Started with the Model
+
+### How to Load and Use the YOLOv9 Instance Segmentation Model
+
+Below is the Python code to load and use the trained YOLOv9 instance segmentation model using the Ultralytics repo:
+
+```python
+import torch
+from ultralytics import YOLO
+
+# Load the trained YOLOv9 model
+model = YOLO('path/to/your/model.pt')  # Update with the correct path to your trained model
+
+# Load an image
+img = 'path/to/your/image.jpg'  # Update with the path to the image you want to use
+
+# Perform instance segmentation
+results = model(img)
+
+# Display results
+results.show()  # Show image with predicted masks
+
+# To get the raw predictions (bounding boxes, masks, etc.)
+for result in results:
+    print(result.boxes)  # Bounding boxes
+    print(result.masks)  # Segmentation masks
+```
+
+### Usage with the HTRflow package
+
+See the model card for [Swedish Lion Libre](https://huggingface.co/Riksarkivet/trocr-base-handwritten-hist-swe-2)
+for example usage with the [HTRflow package](https://github.com/AI-Riksarkivet/htrflow), or refer to the documentation for 
+[HTRflow](https://github.com/AI-Riksarkivet/htrflow)
+
+## Training Details
+
+### Training Data
+
+[Trolldomskommissionen](https://huggingface.co/datasets/Riksarkivet/trolldomskommissionen_seg)
+[Svea Hovrätt](https://huggingface.co/datasets/Riksarkivet/svea_hovratt_seg)
+[Krigshovrättens domböcker](https://huggingface.co/datasets/Riksarkivet/krigshovrattens_dombocker_seg)
+[Jönköpings rådhusrätt och magistrat](https://huggingface.co/datasets/Riksarkivet/jonkopings_radhusratts_och_magistrat_seg)
+[Göta hovrätt](https://huggingface.co/datasets/Riksarkivet/gota_hovratt_seg)
+[Frihetstidens utskottshandlingar](https://huggingface.co/datasets/Riksarkivet/frihetstidens_utskottshandlingar_seg)
+[Bergskollegium relationer och skrivelser](https://huggingface.co/datasets/Riksarkivet/bergskollegium_relationer_och_skrivelser_seg)
+[Bergskollegium advokatfiskalkontoret](https://huggingface.co/datasets/Riksarkivet/bergskollegium_advokatfiskalskontoret_seg)
+
+
+
+### Training Procedure
+
+
+#### Training Hyperparameters
+
+See [training config](https://huggingface.co/Riksarkivet/yolov9-regions-1/blob/main/args.yaml) at model repo
+
+
+## Evaluation
+
+See [training results](https://huggingface.co/Riksarkivet/yolov9-regions-1/blob/main/results.csv)
+
+
+#### Metrics
+
+Standard metrics for instance segmentation. Note that evaluation of segmentation as part of an HTR-pipeline should be measured by what effect it
+has on the following HTR, that is, CER and WER. For implementation and evaluation of entire HTR-pipelines, please check out [HTRflow](https://github.com/AI-Riksarkivet/htrflow),
+the Swedish National Archive's open-source package for HTR and OCR projects.
+
+
+### Model Architecture and Objective
+
+yolov9
+
+#### Software
+
+[Ultralytics](https://github.com/ultralytics)
+
+## Citation [optional]
+
+[YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)