README.md

---
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

<p align="center">
 <img width="400px" src="prediction1.png" alt="qr"/>
</p>

<p align="center">
 <img width="400px" src="prediction2.png" alt="qr"/>
</p>

## Metrics

The final IoU=0.98

The IoU during training
<p align="center">
 <img src="results.png" width="200" />
</p>