Upload crossed out text classifier

README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- pytorch
+- computer-vision
+- ocr
+- crossed-out-text
+library_name: pytorch
+pipeline_tag: image-classification
+---
+
+# crossed-out-text-classifier
+
+## Model Description
+
+This is a ResNet18-based binary classifier trained to detect crossed out text in OCR images. The model classifies images into two categories:
+- `no`: Text is not crossed out
+- `yes`: Text is crossed out
+
+## Model Details
+
+- **Architecture**: ResNet18 with modified classification head
+- **Parameters**: 11,187,158
+- **Input Size**: 224x224 RGB images
+- **Classes**: ['no', 'yes']
+- **Validation Accuracy**: 0.9688
+- **Training Framework**: PyTorch
+
+## Usage
+
+### Using the model directly
+
+```python
+import torch
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Load model
+model = torch.load('pytorch_model.bin', map_location='cpu')
+model.eval()
+
+# Prepare image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+image = Image.open('your_image.png').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+# Make prediction
+with torch.no_grad():
+    outputs = model(input_tensor)
+    probabilities = torch.nn.functional.softmax(outputs, dim=1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+    confidence = torch.max(probabilities, dim=1)[0].item()
+
+class_names = ['no', 'yes']
+print(f"Prediction: {class_names[predicted_class]} (confidence: {confidence:.4f})")
+```
+
+### Using the inference module
+
+```python
+from src.inference import CrossedOutPredictor
+
+# Initialize predictor
+predictor = CrossedOutPredictor()
+predictor.load_model('pytorch_model.bin')
+
+# Make prediction
+prediction, confidence = predictor.predict_image('your_image.png')
+print(f"Prediction: {prediction} (confidence: {confidence:.4f})")
+```
+
+## Training Data
+
+The model was trained on a dataset of OCR images with crossed out and non-crossed out text. The training used:
+- Data augmentation including rotation, scaling, shearing, and color jittering
+- Transfer learning from ImageNet pretrained ResNet18
+- Two-phase training: frozen backbone followed by full fine-tuning
+
+## Limitations
+
+- The model is specifically designed for OCR images and may not generalize well to other image types
+- Performance may vary with different text fonts, sizes, or crossing-out patterns
+- Trained on specific image resolution (224x224) and normalization
+
+## Intended Use
+
+This model is intended for:
+- OCR post-processing pipelines
+- Document analysis systems
+- Text validation workflows
+
+## License
+
+This model is released under the Apache 2.0 license.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{Sleeeepy_crossed_out_text_classifier,
+  title={Crossed Out Text Classifier},
+  author={Your Name},
+  year={2025},
+  howpublished={\url{https://huggingface.co/Sleeeepy/crossed-out-text-classifier}}
+}
+```