Update README.md

README.md

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----
-license: agpl-3.0
----
+---
+license: agpl-3.0
+language:
+- ru
+base_model:
+- Daniil-Domino/yolo11x-text-detection
+library_name: ultralytics
+library_version: 8.3.155
+pipeline_tag: object-detection
+tags:
+- yolo
+- yolo11
+- yolo11x
+- htr
+- text-detection
+- dialectic
+- linguistics
+---
+# Handwritten Russian Dialectic Text Detection using YOLO11
+
+The [YOLO11x-text-detection](https://huggingface.co/Daniil-Domino/yolo11x-text-detection) model was fine-tuned on a dataset of nearly 150 images containing handwritten Russian dialectic texts.
+
+## Model description
+
+YOLO11x-text-detection was fine-tuned for Handwritten Russian Dialectic Text Detection in dialectological cards.
+The model was trained  for 100 epochs with a batch size of 32 using dual NVIDIA T4 GPUs. Fifteen layers were frozen during training as part of the transfer learning process. The entire training took approximately 7 minutes.
+
+# Example Usage
+
+```python
+# Load libraries
+import cv2
+from ultralytics import YOLO
+from pathlib import Path
+import matplotlib.pyplot as plt
+from huggingface_hub import hf_hub_download
+
+
+# Download model
+model_path = hf_hub_download(repo_id="Daniil-Domino/yolo11x-dialectic", filename="model.pt")
+
+# Load model
+model = YOLO(model_path)
+
+# Inference
+image_path = "/path/to/image"
+image = cv2.imread(image_path).copy()
+output = model.predict(image, conf=0.3)
+
+# Draw bounding boxes
+out_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+for data in output[0].boxes.data.tolist():
+    xmin, ymin, xmax, ymax, _, _ = map(int, data)        
+    cv2.rectangle(out_image, (xmin, ymin), (xmax, ymax), color=(0, 0, 255), thickness=3)
+
+# Display result
+plt.figure(figsize=(15, 10))
+plt.imshow(out_image)
+plt.axis('off')
+plt.show()
+
+```
+
+# Metrics
+Below are the key evaluation metrics on the validation set:
+- **Precision**: 0.940
+- **Recall**: 0.924
+- **mAP50**: 0.972
+- **mAP50-95**: 0.656