# Deadline
Deadline for LSA: 08/23/2022 4:00 PM
Deadline for EC Students: 08/30/2022 4:00 PM
# Overview
This challenge concerns the automated classification of images through machine learning techniques. You will work on flower data, where training samples and their ground truth are provided. You will develop suitable classification techniques that will make predictions on unseen test data. The performance will be measured upon submission of the prediction results. A leaderboard will be published after each submission. **You need to use your URN as the Team Name** in the Kaggle page. **Otherwise, your score won't be recorded.** Multiple submissions are encouraged if you wish to improve your models. The latest model for each student will be used for the assessment. You may save multiple prediction results and choose the best one for the final submission.

# Important Rule
You need to use your URN as the Team Name in the Kaggle page. Otherwise, your score won't be recorded.

# Code Submission
You need to submit your code to Surreylearn before the code submission due date (see SurreyLearn for further detail)

# Participation
Please click the join competition button on the page to join it. Otherwise, you cannot submit your result.

# Dataset
### Overview
This is a dataset about **Flower**, and there are 102 types of flowers in this dataset that need to be classified.
### Dataset Size
- Training set: 2040 images
- Testing set: 6149 images
### Training and Testing Data
We will provide the images and labels for the training set and images for the testing set.

# Evaluation
We will evaluate your prediction result on the testing set. So you will need to submit the result in this competition page. **The minimum requirement of this challenge is 60% testing accuracy.**
 
# Submission Format
Your submission should be a csv file, with two columns in it, the first is the id of the test image and the second is its label. We will provide an example of the submission file in the Data Section.