Jovian
Sign In
Learn practical skills, build real-world projects, and advance your career

Human Protein Multi Label Image Classification

This is a starter notebook for the competition Zero to GANs - Human Protein Classification

Exploring the Data

When you create a notebook with the "Notebooks" tab of a Kaggle competition, the data is automatically included in the ../input folder. You can explore the files in the sidebar. Let us create some constants acess the data directories and CSV files.

import os
import torch
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, random_split, DataLoader
from PIL import Image
import torchvision.models as models
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
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
DATA_DIR = '../input/jovian-pytorch-z2g/Human protein atlas'

TRAIN_DIR = DATA_DIR + '/train'                           # Contains training images
TEST_DIR = DATA_DIR + '/test'                             # Contains test images

TRAIN_CSV = DATA_DIR + '/train.csv'                       # Contains real labels for training images
TEST_CSV = '../input/jovian-pytorch-z2g/submission.csv'   # Contains dummy labels for test image

The train.csv file contains image IDs and labels for training data. Note that this is a multi-label classification problem. Each image can have more than one type of protein.