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---
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. |