train_transform = # YOUR CODE FOR AUGMENTATIONS val_transform = # YOUR CODE FOR VALIDATION AUGMENTATIONS # HINT: TRAIN TRANSFORM OBVIOUSLY SHOULD BE HARDER THAN THOSE FOR VALIDATION train_dataset = FruitDataset("./train_zip/train", transform=train_transform) val_dataset = FruitDataset("./test_zip/test", transform=val_transform) model = # YOUR CODE, CREATE MODEL FOR OBJECT DETECTION # HINT: YOU CAN USE torchvision.models AND torchvision.models.detection # READ OFFICIAL DOCS FOR MORE INFO optimizer = # SELECT YOUR OPTIMIZER train_dataloader = # CREATE YOUR DATALOADER, SELECT APPROPRIATE batch_size val_dataloader = # CREATE VALIDATION DATALOADER n_epochs = # SELECT APPROPRIZTE NUMBER OF EPOCHS device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") train(model, train_dataloader, val_dataloader, optimizer, device, n_epochs) #https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week03/SGA1_Object_Detection.ipynb
train_transform = # YOUR CODE FOR AUGMENTATIONS val_transform = # YOUR CODE FOR VALIDATION AUGMENTATIONS # HINT: TRAIN TRANSFORM OBVIOUSLY SHOULD BE HARDER THAN THOSE FOR VALIDATION train_dataset = FruitDataset("./train_zip/train", transform=train_transform) val_dataset = FruitDataset("./test_zip/test", transform=val_transform) model = # YOUR CODE, CREATE MODEL FOR OBJECT DETECTION # HINT: YOU CAN USE torchvision.models AND torchvision.models.detection # READ OFFICIAL DOCS FOR MORE INFO optimizer = # SELECT YOUR OPTIMIZER train_dataloader = # CREATE YOUR DATALOADER, SELECT APPROPRIATE batch_size val_dataloader = # CREATE VALIDATION DATALOADER n_epochs = # SELECT APPROPRIZTE NUMBER OF EPOCHS device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") train(model, train_dataloader, val_dataloader, optimizer, device, n_epochs) #https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week03/SGA1_Object_Detection.ipynb
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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train_transform = # YOUR CODE FOR AUGMENTATIONS
val_transform = # YOUR CODE FOR VALIDATION AUGMENTATIONS
# HINT: TRAIN TRANSFORM OBVIOUSLY SHOULD BE HARDER THAN THOSE FOR VALIDATION
train_dataset = FruitDataset("./train_zip/train", transform=train_transform)
val_dataset = FruitDataset("./test_zip/test", transform=val_transform)
model = # YOUR CODE, CREATE MODEL FOR OBJECT DETECTION
# HINT: YOU CAN USE torchvision.models AND torchvision.models.detection
# READ OFFICIAL DOCS FOR MORE INFO
optimizer = # SELECT YOUR OPTIMIZER
train_dataloader = # CREATE YOUR DATALOADER, SELECT APPROPRIATE batch_size
val_dataloader = # CREATE VALIDATION DATALOADER
n_epochs = # SELECT APPROPRIZTE NUMBER OF EPOCHS
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
train(model, train_dataloader, val_dataloader, optimizer, device, n_epochs)
#https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week03/SGA1_Object_Detection.ipynb
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