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1import torch
2from PIL import Image
3import torchvision.transforms as transforms
4
5# Load model
6model = torch.load('pytorch_model.bin', map_location='cpu')
7model.eval()
8
9# Preprocessing
10transform = transforms.Compose([
11 transforms.Resize((256, 256)),
12 transforms.ToTensor(),
13 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
14])
15
16# Inference
17image = Image.open('grain_image.jpg').convert('RGB')
18input_tensor = transform(image).unsqueeze(0)
19
20with torch.no_grad():
21 outputs = model(input_tensor)
22
23print(f"Count: {outputs['count'].item():.1f}")
24print(f"Good grains: {outputs['good'].item():.1f}")
25print(f"Bad grains: {outputs['bad'].item():.1f}")
26print(f"Quality: {'Good' if outputs['quality'].argmax().item() == 1 else 'Bad'}")1@misc{grain_classifier_2025,
2 title={Grain Quality Classification Model},
3 author={Your Name},
4 year={2025},
5 howpublished={\url{https://huggingface.co/Hk4crprasad/grain-quality}}
6}