1. The trained model (in any file format of their choice).
2. The code used for training the model including any pre-processing and post-processing in train.py file.
3. The code for inference that will take a folder path (relative to where the code will be run from) as a command line input. The file should be named inference1.py for task 1 and inference2.py for task 2. Each file should automatically load the respective model, read test images from the given folder and generate a <Team Name> _i.csv (i=Task number) file containing columns “Image Name” and, “Label” for task 1 or “Result” for task 2 (without quotes). [Note: The model should produce integers (+ve or -ve) only in the “Result” column. In case you are not attempting a part, skip the code file for that part.]
4. The Readme.md describing briefly the methods used, installations and insights and any other points you would like to include.
5. The Declaration.txt file. If the work is completely your own without taking any help from a person or site pertaining specifically to the problem, then state that ”The work submitted is completely our/mine own.” Else state all the sources you have taken help from. Plagiarism is not welcome.
6. The requirements.txt that contains all the libraries required to be installed to run the code.

Upload the zipped folder to Google Drive (of any member from your team) and submit a share-able link under the ``Additional Info" tab while submitting your project at Devpost. The name of your project is equivalent to the team name. Please make only one project. You can keep updating/changing it till the deadline. Make sure all changes are reflected on your Drive and through the link you are submitting.