Index Card Classifier

A fine-tuned image classifier for detecting library index cards vs other image types (verso/back of cards, covers, blank pages).

Model Details

Base Model: efficientnet_b0
Task: Binary classification (index_card vs other)
Training Data: 70 images
Validation Data: 30 images
Framework: PyTorch + timm

Training Data Distribution

{
  "cover": 1,
  "blank": 1,
  "needs_review": 1,
  "verso": 49,
  "index_card": 46,
  "other": 2
}

Performance

Metric	Value
Validation Accuracy	96.7%
Validation F1 (index_card)	0.966
Validation F1 (other)	0.968

Classification Report

              precision    recall  f1-score   support

       other      1.000     0.938     0.968        16
  index_card      0.933     1.000     0.966        14

    accuracy                          0.967        30
   macro avg      0.967     0.969     0.967        30
weighted avg      0.969     0.967     0.967        30

Confusion Matrix

[[15  1]
 [ 0 14]]

Usage

import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from safetensors.torch import load_file
from torchvision import transforms

# Download and load model from Hub
weights_path = hf_hub_download(
    repo_id="davanstrien/nls-index-card-classifier",
    filename="classifier.safetensors"
)
model = timm.create_model('efficientnet_b0', pretrained=False, num_classes=2)
model.load_state_dict(load_file(weights_path))
model.eval()

# Preprocess
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]),
])

# Inference
image = Image.open('card.jpg').convert('RGB')
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(input_tensor)
    probs = torch.softmax(output, dim=1)
    pred = output.argmax(1).item()
    confidence = probs[0, pred].item()

classes = ['other', 'index_card']
print(f"Prediction: {classes[pred]} ({confidence:.1%})")

Training

Trained using frozen backbone with only classifier head fine-tuned.

python train_classifier.py --model efficientnet_b0 --epochs 20 --val-split 0.3

Context

This model was developed for the National Library of Scotland to help process digitized manuscript index cards from the Advocate's Library collection.

License

Apache 2.0

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