Deep Learning Bootcamp - Assignment 1 - Intermediate: Object Recognition
Image recognition is a vital component in robotics such as driverless vehicles or domestic robots. Image recognition is also important in image search engines such as Google or Bing image search whereby you use rich image content to query for similar stuff. Like in Google photos where the system uses image recognition to categorize your images into things like cats, dogs, people and so on so that you can quickly search your albums for things like, “give me photos of my cat”, that's awesome.
Ever noticed how Facebook instantly recognises your friend’s face and asks you if you want to tag him in that photo? That’s image recognition. That’s just a basic example.
You are working on a robotics project where you are required to train your robot so that it can recognize different images. Your task here is to build a deep learning model that helps you recognize the object in images and predict the class of the image. (class ranges from 1 to 10).
Submissions are evaluated using Accuracy Score. How do we do it?
Once you generate and submit the target variable predictions on the test dataset, your submissions will be compared with the true values of the target variable.
The True or Actual values of the target variable are hidden on the DPhi platform so that we can evaluate your model's performance on unseen data. Finally, an accuracy score for your model will be generated and displayed.
Submission Deadline: 6th September 2020
About the Data
The training dataset consists of 50,000 32X32 colour images of 10 different objects. These are the different classes of images in the dataset.
- Truck To load the training dataset, run the below commands
from tensorflow.keras.datasets.cifar10 import load_data (x_train, y_train), (_, __) = load_data()
To load the test data, run the below commands Note: The test data is already flattened to a vector. The test data is in csv format where each row represents one image.
import pandas as pd test_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/cifar_image_flattened_pixels.csv")
To participate in this challenge either you have to create a team of atleast None members or join some team