---
license: mit
datasets:
- cifar10
library_name: keras
pipeline_tag: image-classification
---
### Model Name: `Enhanced-CIFAR10-CNN`

**Description:**

Introducing `Enhanced-CIFAR10-CNN`, a state-of-the-art Convolutional Neural Network (CNN) trained on the CIFAR dataset. Based on extensive research, with an impressive accuracy of 89%, this model sets a new benchmark in image classification tasks. What sets it apart?

- **High Performance**: Achieves an accuracy rate of 86%, surpassing standard benchmarks.

- **Fast Inference**: Optimized for speed, this model ensures quick predictions without compromising on accuracy.

- **Compact Size**: Its small footprint makes it ideal for edge deployments and integration into existing systems.

- **Transfer Learning Ready**: The model's architecture and pre-trained weights make it an excellent candidate for fine-tuning and further development in various applications.

**Usage Examples:**

```python
from keras.models import load_model

# Load the model
model = load_model('path/to/enhancedCIFAR-10-CNN.h5')

# Perform inference
result = model.predict(input_data)
```

**Dependencies:**

- Keras >= 2.4.0
- TensorFlow >= 2.5.0

**Citation:**
Ogundokun, Roseline Oluwaseun, et al. "Improved CNN based on batch normalization and adam optimizer." International Conference on Computational Science and Its Applications. Cham: Springer International Publishing, 2022.
If you find this model useful, please cite our work.