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README.md

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+---
+title: Grain Quality Classifier
+emoji: 🌾
+colorFrom: green
+colorTo: yellow
+sdk: pytorch
+app_file: app.py
+pinned: false
+license: mit
+tags:
+- grain
+- agriculture
+- computer-vision
+- quality-control
+- multi-task-learning
+- pytorch
+---
+
+# Grain Quality Classification Model 🌾
+
+A multi-task deep learning model for grain quality control, trained on Tesla T4 GPU.
+
+## Model Description
+
+This model performs multi-task learning for grain quality assessment:
+- **Count Prediction**: Estimates total grain count in images
+- **Good Grain Count**: Counts high-quality grains
+- **Bad Grain Count**: Counts low-quality/damaged grains  
+- **Quality Classification**: Binary classification (good/bad dominant)
+
+## Architecture
+
+- **Backbone**: ResNet-50 (pre-trained on ImageNet)
+- **Input Size**: 256x256 RGB images
+- **Multi-task heads**: Separate heads for each prediction task
+- **Training**: Mixed precision on Tesla T4 GPU
+
+## Training Details
+
+- **Epochs**: 73
+- **Best Validation Loss**: 23.005850791931152
+- **Optimizer**: AdamW with OneCycleLR scheduler
+- **Data Augmentation**: Extensive augmentations for robustness
+- **GPU**: Tesla T4 with mixed precision training
+
+## Usage
+
+```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()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Inference
+image = Image.open('grain_image.jpg').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    outputs = model(input_tensor)
+    
+print(f"Count: {outputs['count'].item():.1f}")
+print(f"Good grains: {outputs['good'].item():.1f}")
+print(f"Bad grains: {outputs['bad'].item():.1f}")
+print(f"Quality: {'Good' if outputs['quality'].argmax().item() == 1 else 'Bad'}")
+```
+
+## Model Performance
+
+- Trained on agricultural grain dataset
+- Multi-task learning approach
+- Optimized for real-time quality control applications
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{grain_classifier_2025,
+  title={Grain Quality Classification Model},
+  author={Your Name},
+  year={2025},
+  howpublished={\url{https://huggingface.co/Hk4crprasad/grain-quality}}
+}
+```
+
+## License
+
+MIT License - See LICENSE file for details.