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:
from keras.models import load_model
# Load the model
model = load_model('path/to/jsotiro-cnn-cifar.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.