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
| Metric | Value |
|---|---|
| Accuracy | 0.921 |
| F1 Score | 0.918 |
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}}
}
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timm/tf_efficientnetv2_m.in21k