Model Card for Model ID
Convolutional Neural Network (CNN) for CIFAR-10 Dataset
This repository contains a Convolutional Neural Network (CNN) model trained on the CIFAR-10 dataset. CIFAR-10 is a popular benchmark dataset in the field of computer vision, consisting of 60,000 32x32 color images in 10 classes, with 6,000 images per class.
Model Architecture:
The CNN model architecture consists of several convolutional layers followed by max-pooling layers, batch normalization, and ReLU activation functions. The final layers typically include one or more fully connected layers followed by a softmax layer for classification.
Dataset:
CIFAR-10 dataset is used for training and evaluation. It comprises 50,000 training images and 10,000 test images across 10 classes, including airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
Training:
The model is trained using stochastic gradient descent (SGD) or other optimization algorithms with appropriate hyperparameters such as learning rate, batch size, and number of epochs.
Evaluation:
The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score on a separate validation set or through cross-validation.
Usage:
The trained model can be used for various computer vision tasks, including image classification, object detection, and image segmentation.
- Developed by: Irfan Arshad
- Funded by [optional]: PAF-IAST
- Shared by [optional]: Irfan Arshad
- Model type: Classification Model
- Language(s) (NLP): Python Tensorflow Deep Learning
Results
Software
Google Colab Visual Studio Code Web Browser
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