**Google Colab Notebook link:**
https://colab.research.google.com/drive/1iA8nvb93VLcrDfIt17AOIHnkVdLSNcW_?usp=sharing

This repo contains files for defining and creating a simple convolutional network for 
classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 degrees). 
The following files are in this repo:

Coding_Challenge_for_Fatima_Fellowship.ipynb -- a copy of the Google Collab notebook with the code/output/writeup

best_model.pth -- dictionary of best model stats/weights found during training

cifar10flip_trn.pt -- saved training dataset of ~50% flipped CIFAR10 images

cifar10flip_tst.pt -- saved training dataset of ~50% flipped CIFAR10 images

image_examples.png -- an array of example imags from flipped CIFAR10 dataset

write-up -- write up of data processing, model results, and potential improvements (also in Google Colab)

wrong_predictions.zip -- a zip file of PNG images that were incorrectly classified by my model
(each file name provide information on the image's prediction, true label, and its class)
{}
region:us
TAGS
#region-us 

Google Colab Notebook link:
URL

This repo contains files for defining and creating a simple convolutional network for 
classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 degrees). 
The following files are in this repo:

Coding_Challenge_for_Fatima_Fellowship.ipynb -- a copy of the Google Collab notebook with the code/output/writeup

best_model.pth -- dictionary of best model stats/weights found during training

cifar10flip_trn.pt -- saved training dataset of ~50% flipped CIFAR10 images

cifar10flip_tst.pt -- saved training dataset of ~50% flipped CIFAR10 images

image_examples.png -- an array of example imags from flipped CIFAR10 dataset

write-up -- write up of data processing, model results, and potential improvements (also in Google Colab)

wrong_predictions.zip -- a zip file of PNG images that were incorrectly classified by my model
(each file name provide information on the image's prediction, true label, and its class)
TAGS
#region-us 

passage: TAGS
#region-us 
