Create README.md

README.md

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+
+## How to Run and Test the Watermark Removal Model
+
+### Setup and Training
+
+1. **Install dependencies** (run once):
+   ```bash
+   !pip install -U gdown ultralytics wandb scikit-learn requests
+   ```
+
+2. **Mount Google Drive and set working directory**:
+   ```python
+   from google.colab import drive
+   drive.mount('/content/drive', force_remount=False)
+   import os
+   os.chdir('/content/drive/MyDrive/Colab/Watermark_remover')
+   ```
+
+3. **Download and prepare datasets**  
+   The script downloads watermark datasets from Google Drive, extracts them, and collects images for watermarking.
+
+4. **Generate watermarked images and YOLO labels**  
+   Watermarks are added to images with bounding box labels created in YOLO format.
+
+5. **Split dataset into training and validation sets** and create `data.yaml` for YOLOv11 training.
+
+6. **Train the YOLOv11 model** with augmentations and tuned hyperparameters:
+   ```python
+   from ultralytics import YOLO
+   import wandb
+
+   wandb.login()  # Login to Weights & Biases for experiment tracking
+
+   model = YOLO("yolo11m.pt")  # Load YOLOv11m base model
+
+   model.train(
+       data="data.yaml",
+       epochs=100,
+       batch=16,
+       imgsz=640,
+       project="logo_detection",
+       name="yolo11m_logo_run",
+       exist_ok=True,
+       save=True,
+       save_txt=True,
+       augment=True,
+       hsv_h=0.015,
+       hsv_s=0.7,
+       fliplr=0.5,
+       mixup=0.1,
+       mosaic=1.0,
+       scale=0.5,
+       shear=0.0,
+       perspective=0.0,
+       translate=0.1
+   )
+   ```
+
+### Testing and Visualization
+
+1. **Load the trained model weights**:
+   ```python
+   from ultralytics import YOLO
+   model = YOLO("logo_detection/yolo11m_logo_run/weights/best.pt")
+   ```
+
+2. **Select test images** from the validation set:
+   ```python
+   from pathlib import Path
+   import random
+
+   test_folder = Path("dataset/images/val")
+   test_images = list(test_folder.glob("*.*"))
+   test_images = random.sample(test_images, min(10, len(test_images)))
+   ```
+
+3. **Run detection and watermark removal with visualization**:
+   ```python
+   import cv2
+   import numpy as np
+   import matplotlib.pyplot as plt
+
+   def visualize_detection_and_removal(model, img_path):
+       results = model(str(img_path))[0]
+       img = cv2.imread(str(img_path))
+       img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+       # Draw detection boxes
+       img_boxes = img.copy()
+       for box in results.boxes:
+           xyxy = box.xyxy[0].cpu().numpy().astype(int)
+           cv2.rectangle(img_boxes, (xyxy[0], xyxy[1]), (xyxy[2], xyxy[3]), (0,255,0), 2)
+
+       # Create mask for inpainting
+       mask = np.zeros(img.shape[:2], dtype=np.uint8)
+       for box in results.boxes:
+           xyxy = box.xyxy[0].cpu().numpy().astype(int)
+           x1, y1, x2, y2 = xyxy
+           mask[y1:y2, x1:x2] = 255
+
+       # Remove watermark using inpainting
+       inpainted = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)
+       inpainted_rgb = cv2.cvtColor(inpainted, cv2.COLOR_BGR2RGB)
+
+       # Display images
+       plt.figure(figsize=(15,5))
+       plt.subplot(1,3,1)
+       plt.title("Original Image")
+       plt.imshow(img_rgb)
+       plt.axis('off')
+
+       plt.subplot(1,3,2)
+       plt.title("Detected Logos")
+       plt.imshow(cv2.cvtColor(img_boxes, cv2.COLOR_BGR2RGB))
+       plt.axis('off')
+
+       plt.subplot(1,3,3)
+       plt.title("Watermark Removed")
+       plt.imshow(inpainted_rgb)
+       plt.axis('off')
+       plt.show()
+
+   for img_path in test_images:
+       print(f"Testing image: {img_path.name}")
+       visualize_detection_and_removal(model, img_path)
+   ```
+
+---
+
+### Summary
+
+- This repository provides a pipeline to generate watermarked images with YOLO labels, train a YOLOv11 model to detect logos/watermarks, and remove them using inpainting.
+- Training is done in Colab with Google Drive for storage.
+- Testing visualizes detection and watermark removal results on sample validation images.
+
+
+Citations:
+[1] https://huggingface.co/templates/model-card-example/blob/f0ce9d5d178c10e164d406868f72b1f2f2158cde/README.md
+[2] https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
+[3] https://huggingface.co/docs/hub/en/model-cards
+[4] https://huggingface.co/templates/model-card-example/blame/f0ce9d5d178c10e164d406868f72b1f2f2158cde/README.md
+[5] https://machinelearninglibrarian.substack.com/p/2023-03-07-readme-templatehtml
+[6] https://huggingface.co/templates/model-card-example/commit/f0ce9d5d178c10e164d406868f72b1f2f2158cde
+[7] https://huggingface.co/learn/llm-course/en/chapter4/4
+[8] https://huggingface.co/SEBIS/code_trans_t5_base_code_documentation_generation_ruby/blame/2a39c4e86977714a6ed4aab478098a43e9751e05/README.md
+
+---
+Answer from Perplexity: pplx.ai/share