Learn practical skills, build real-world projects, and advance your career
Updated 4 years ago
Fruit Classification
import torch
import matplotlib.pyplot as plt
import numpy as np
import os
import cv2
from torchvision.datasets import ImageFolder
from torchvision import transforms as T
from torch.utils.data import DataLoader
from torchvision.utils import make_grid
import torch.nn.functional as F
import torch.nn as nn
import jovian
path = "data/fruits-360"
train_path = path + '/Training2'
test_path = path + '/Test2'
train_transforms = T.Compose([
T.ToTensor()
])
dataset = ImageFolder(train_path, transform=train_transforms)
train_dataset, test_dataset = torch.utils.data.random_split(dataset, [32316 - 8079,8079])
batch_size = 32
train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True,num_workers=3,pin_memory=True)
val_dl = DataLoader(test_dataset, batch_size=batch_size, shuffle=False,num_workers=3,pin_memory=True)