Initial Commit to Hugging Face, Submitting NN for trained on CIFAR10.

README.md

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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,
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6319030647a84df2a5dd106c/M1a1cCnq9cQBminzejk0r.png)
+
+
+## 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