bmayank / finalproject

Machine Learning - Final Assignment - Capstone Project

finalproject

Machine Learning - Final Assignment - Capstone Project

I have attached following files with the project. Folders

1. Training folder  Contains 10 training folders from n0 to n9
2. Validation folders  Contains 10 validation folders from n0 to n9

I couldn't create the folders and files due to huge size. Thus, I am providing the zip repository of the file. We need to unzip the folder and keep the files in the following folder structure:

1. ./training/n0

2. ./training/n1

3. ...

4. ./training/n9

5. ./validation/n0

6. ./validation/n1

7. ...

8. ./validation/n9

I have used 'Dog Breed' workspace to create this project. Details could be checked under folder 'Capstone_Project'.

Files

1. Proposal.pdf  Original proposal document
2. Capstone - Project Report.pdf  Project report
3. Monkey_labels.csv  File containing details of the input files
4. Monkey_species.ipynb  Python notebook containing python code with sample data run
5. Monkey_species.html  HTML version of python code with sample data run
6. Monkey_species.pdf  PDF version of python code with sample data run

I would like to call out various resources which influences my project

1. Fine-Grained Categorization: https://vision.cornell.edu/se3/fine-grained-categorization/
2. Fine-Grained Categorization: https://www.researchgate.net/publication/301452581_Croatian_Fish_Dataset_Fine-grained_classification_of_fish_species_in_their_natural_habitat
3. https://www.kaggle.com/slothkong/10-monkey-species: There were various samples / recommendataions. They indeed help me think through my approach and how I go about the problem.
4. Fig Ref: https://towardsdatascience.com/epoch-vs-iterations-vs-batch-size-4dfb9c7ce9c9

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Additional References 5. Ref: https://isaacchanghau.github.io/post/loss_functions/ 6. Fig Source: https://www.cs.toronto.edu/~frossard/post/vgg16/ 7. Google for ResNet50 information