coffeebean-quality-vit

☕ Arabica Coffee Bean Quality Classification — ViT & MobileNetV2

Trained models from undergraduate thesis research on classifying Arabica coffee bean quality using Vision Transformer (ViT), benchmarked against MobileNetV2 (CNN baseline). Classification follows the Indonesian National Standard (SNI) defect value system across 6 quality grades.

📄 Full paper & poster → GitHub Releases 💻 Code & notebooks → GitHub Repo 📦 Dataset → Kaggle

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🏆 Model Files

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🗂️ Quality Classes (SNI)

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🚀 Usage

Load best model (ViT ImageNet B/16, 70:30)

import torch
import torchvision.models as models
from torchvision import transforms
from PIL import Image

# 1. Setup model
class_names = ['mutu1', 'mutu2', 'mutu3', 'mutu4', 'mutu5', 'mutu6']
model = models.vit_b_16(weights=None)
model.heads = torch.nn.Sequential(
    torch.nn.Dropout(0.1),
    torch.nn.Linear(768, len(class_names))
)

# 2. Load weights (download from this repo first)
checkpoint = torch.load('vit-imgt-16(7030).pth', map_location='cpu')
model.load_state_dict(checkpoint)
model.eval()

# 3. Preprocessing (ImageNet standard)
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]
    )
])

# 4. Predict
image = Image.open('your_coffee_image.jpg')
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    outputs = model(input_tensor)
    probabilities = torch.softmax(outputs, dim=1)
    predicted_idx = torch.argmax(probabilities).item()

print(f"Predicted: {class_names[predicted_idx]}")
print(f"Confidence: {probabilities[0, predicted_idx].item():.4f}")

Load MobileNetV2 baseline

import torchvision.models as models

class_names = ['mutu1', 'mutu2', 'mutu3', 'mutu4', 'mutu5', 'mutu6']
model = models.mobilenet_v2(weights=None)
model.classifier[1] = torch.nn.Linear(model.last_channel, len(class_names))

checkpoint = torch.load('mobilenetv2(7030)weight.pth', map_location='cpu')
model.load_state_dict(checkpoint)
model.eval()

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⚙️ Training Details

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📊 Key Findings

• ViT ImageNet B/16 outperforms MobileNetV2 by +5% accuracy
• ViT is also +2 images/second faster at inference
• Tradeoff: ViT model size is ~320 MB larger than MobileNetV2

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📄 Citation

@thesis{zaafirrahman2024vit,
  author  = {Aulya Az Zaafirrahman},
  title   = {Klasifikasi Mutu Biji Kopi Arabika Berbasis Image Processing Menggunakan Metode Vision Transformer (ViT)},
  school  = {Universitas Brawijaya},
  type    = {Teknik Industri Pertanian},
  year    = {2024}
}