!pip install jovian --upgrade --quietFor the course project, you will pick a dataset of your choice and apply the concepts learned in this course to train deep learning models end-to-end with PyTorch, experimenting with different hyperparameters & metrics.
Dataset choice
References
I chose a Kaggle dataset dealing with Recursion Cellular Image Classification (https://www.kaggle.com/xhlulu/recursion-cellular-image-classification-224-jpg).
Context (adapted from Kaggle)
The cost of some drugs and medical treatments has risen so high in recent years that many patients are having to go without.
One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatments takes, on average, more than 10 years and costs hundreds of millions of dollars.
Recursion Pharmaceuticals, creators of the industry’s largest dataset of biological images, generated entirely in-house, believes AI has the potential to dramatically improve and expedite the drug discovery process. More specifically, your efforts could help them understand how drugs interact with human cells.
Objective
This is a classification project of images:I'll classify images of cells under one of 1,108 different genetic perturbations.
If successful, this project could dramatically improve the industry’s ability to model cellular images according to their relevant biology, decrease the cost of treatments and ensure these treatments get to patients faster.
from google.colab import files
import zipfile
import pandas as pd
import torch
from torch.utils.data import Dataset, random_split, DataLoader
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import os
import torch
import numpy as np
from PIL import Image
import torchvision.models as models
from sklearn.metrics import f1_score
import torch.nn.functional as F
import torch.nn as nn
from torchvision.utils import make_grid
%matplotlib inline# Install Kaggle library
!pip install -q kaggle