indian-monuments-cnn-model

Indian Monuments CNN Model

This model is a fine-tuned image classifier for recognizing major Indian monuments and architectural styles using EfficientNetV2-M as the base. It leverages transfer learning with Keras/TensorFlow, trained on the danushkumarv/indian-monuments-image-dataset (24 classes).

Model Details

• Model Type: Transfer Learning (EfficientNetV2-M)
• Task: Image Classification (24 classes)
• Base Model: timm/tf_efficientnetv2_m.in21k
• Dataset: danushkumarv/indian-monuments-image-dataset
• Framework: Keras / TensorFlow

Results

Intended Uses

• Classification: Predict image class for Indian monuments.
• Feature Extraction: Use EfficientNet backbone for CV tasks.
• Education: Integrate into apps/sites for learning about Indian heritage.

Limitations and Bias

• Out-of-Distribution: Best for Indian monuments; may misclassify non-monument objects or unusual conditions.
• Class Imbalance: Accuracy may favor classes with more samples.
• Fine-Grained Recognition: Not for identifying sub-parts or rooms within monuments.

How to Use

import keras
from huggingface_hub import from_pretrained_keras

repo_id = "koyelog/indian-monuments-cnn-model"
model = from_pretrained_keras(repo_id)

import numpy as np
from PIL import Image

def preprocess_image(image_path, target_size=(224, 224)):
    img = Image.open(image_path).convert('RGB')
    img = img.resize(target_size)
    img_array = np.asarray(img, dtype=np.float32)
    img_array = img_array / 255.0
    return np.expand_dims(img_array, axis=0)

x = preprocess_image('path/to/your/monument.jpg')
predictions = model.predict(x)
predicted_class_index = np.argmax(predictions[0])

# Define your class name mapping
class_names = [
    "Taj Mahal", "Red Fort", "Charminar", # ...add all class names
]
print(f"Predicted Monument: {class_names[predicted_class_index]}")

Citation

@misc{koyelog_indian_monuments_cnn_model,
  title={Indian Monuments CNN Model},
  author={koyelog},
  year={2025},
  howpublished={\url{https://huggingface.co/koyelog/indian-monuments-cnn-model}}
}

Model Card Authors

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