19c Newspaper Column Yolo

--- tags: - object-detection - yolo - ultralytics - historical-newspapers --- # 19c Newspaper Column YOLO Detector This repository contains a YOLO detector fine-tuned to detect and segment columns in 19th-century American newspaper pages. Extracting individual columns is a critical pre-processing step for historical OCR pipelines. Slicing long columns into smaller, overlapping horizontal strips resolves the text-hallucination/repetition issues that modern Vision-Language Models (like Gemma 2B/27B) encounter on full-page layouts. ## Model Training & Performance The model was trained on annotated historical newspaper pages. You can view the training metrics, curves, and validation performance directly below: ### 1. Training Metrics (`results.png`) Shows training/validation loss decay and precision/recall improvements over epochs:  ### 2. Confusion Matrix Displays normalized classification performance:  ### 3. Model Predictions vs. Ground Truth Compare the validation batch labels (ground truth annotations) with the actual predictions generated by the trained model: | Ground Truth Labels (`val_batch0_labels.jpg`) | Model Predictions (`val_batch0_pred.jpg`) | | --- | --- | |  |  | --- ## How to Run This Model You can easily download and run this model in Python using the `ultralytics` package. ### Installation ```bash pip install ultralytics huggingface_hub ``` ### Python Inference Code ```python from huggingface_hub import hf_hub_download from ultralytics import YOLO # 1. Download the weights from Hugging Face model_path = hf_hub_download(repo_id="ambrosfitz/19c-newspaper-column-yolo", filename="best.pt") # 2. Load the model model = YOLO(model_path) # 3. Perform detection on a newspaper page image results = model("path_to_newspaper_page.jpg") # 4. Display or save the segmented columns results[0].show() # results[0].save(filename="output.jpg") ```