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README.md

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----
-license: mit
-language:
-- en
-metrics:
-- accuracy
-- f1
-pipeline_tag: image-classification
-library_name: keras
-tags:
-- keras
-- classification
-- image_classification
-- CNN
-- image_recognition
-base_model:
-- timm/tf_efficientnetv2_m.in21k
----
-
-# 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](https://www.kaggle.com/datasets/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](https://www.kaggle.com/datasets/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
-
-```python
-import keras
-from huggingface_hub import from_pretrained_keras
-
-repo_id = "koyelog/indian-monuments-cnn-model"
-model = from_pretrained_keras(repo_id)
-```
-
-```python
-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
-
-Model card generated by [koyelog](https://huggingface.co/koyelog)