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Updated 3 years ago
# !pip install jovian --upgrade --quiet
Facial Keypoints Detection
Lets try to apply the basic convolutional deep learning concepts to the Facial Keypoints Detection Kaggle Competition and see how our model performs.
The link to the competition is -> https://www.kaggle.com/c/facial-keypoints-detection/overview/description
The objective of the task is to predict key points on face images.
This can be used for a variety of application like -
- tracking faces in image and video
- analysing facial expressions
- detecting dysmorphic facial signs for medical diagnosis
- biometrics / face recognition
Detecing facial keypoints is a very challenging problem. Facial features vary greatly from one individual to another, and even for a single individual, there is a large amount of variation due to 3D pose, size, position, viewing angle, and illumination conditions.
import os
from google.colab import files
import pandas as pd
import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
from datetime import datetime
import torch.nn.functional as F
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
#import kaggle
%matplotlib inline
# Project name used for jovian.commit
project_name = 'my-course-project'
Getting the Kaggle dataset
Let's first install the kaggle api.