import torch
from torchvision import transforms
from PIL import Image

# Load model (define your HybridModel class first)
model = HybridModel(num_classes=38)
model.load_state_dict(torch.load("best_hybrid_resnet_densenet.pth", map_location="cpu"))
model.eval()

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

img = transform(Image.open("leaf.jpg")).unsqueeze(0)
with torch.no_grad():
    logits = model(img)
    probs = torch.softmax(logits, dim=1)[0]
    top_p, top_idx = torch.topk(probs, k=3)
    
    for i in range(len(top_p)):
        print(f"Class: {top_idx[i].item()}, Confidence: {top_p[i].item():.2f}")

prathamrajbhar11/ArogyaDrishti-crop-disease

🔬 ArogyaDrishti — Crop Disease Detection Model

Architecture

Model Files

Inputs

Output

Usage

prathamrajbhar11/
ArogyaDrishti-crop-disease