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---
license: mit
datasets:
- cifar10
language:
- en
pipeline_tag: image-classification
---
# Image Classifier (trained on CIFAR10)
The model aims to classify images from this dataset into 1 of 10 classes, in which we build a model on the training set & evaluate it on the test set.
The dataset include 10 classes which are:
- airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks
with roughly 60,000 images.
CIFAR10 models exist and so the aim here is not for a model that is identical and easy to train, this model has a unique architecture which will be explained.
## Model Details
- **Developed by:** Michael Peres
- **Model type:** Image Classification of CIFAR10 dataset.
- **Language(s) (NLP):** Michael Peres
- **License:** MIT
### Model Architecture
This model has a more unique approach for the architecture,
## Uses
This model is just intended as a learning challenge where CIFAR10 is trained on a unconventional architecture.
## How to Get Started with the Model
Use the code below to get started with the model.
Look at provided `main.py` which contains the model and the training code, if you would like to train it.
We are using optuna, to tune the hyperparameters.
ore Information Needed]
These are the evaluation metrics being used, ideally with a description of why. -->
## Model Card Contact
https://github.com/makiisthenes
ec20433@qmul.ac.uk |