metadata

pipeline_tag: object-detection
tags:
  - ultralytics
  - yolo
  - yolov8
  - tracking
  - image-classification
  - obb
  - object-detection
language:
  - hy
datasets:
  - armvectores/handwritten_text_detection

YOLOv8 Handwritten Text Detection

Model Description

YOLOv8 is the eighth version of the You Only Look Once (YOLO) object detection algorithm. It excels in speed and accuracy, making it an ideal choice for real-time applications. The YOLOv8 model provided here has been fine-tuned on a diverse dataset of handwritten texts to improve its specificity in detecting handwritten content as opposed to typed or printed materials.

How to use

pip install ultralytics

from ultralytics import YOLO
from huggingface_hub import hf_hub_download
from matplotlib import pyplot as plt

# Load the weights from our repository
model_path = hf_hub_download(local_dir=".",
                             repo_id="armvectores/yolov8n_handwritten_text_detection",
                             filename="best.pt")
model = YOLO(model_path)

# Load test blank
test_blank_path = hf_hub_download(local_dir=".",
                             repo_id="armvectores/yolov8n_handwritten_text_detection",
                             filename="test_blank.png")

# Do the predictions
res = model.predict(source=test_blank_path, project='.',name='detected', exist_ok=True, save=True, show=False, show_labels=False, show_conf=False, conf=0.5, )

plt.figure(figsize=(15,10))
plt.imshow(plt.imread('detected/test_blank.png'))
plt.show()

Tests

Metrics

The final IoU=0.98

The IoU during training