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Transfer Learning for Computer Vision Tutorial#
Created On: Mar 24, 2017 | Last Updated: Jan 27, 2025 | Last Verified: Nov 05, 2024
Author: Sasank Chilamkurthy
In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You can read more about the transfer learning at cs231n notes
Quoting these notes,
In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.
These two major transfer learning scenarios look as follows:
Finetuning the ConvNet: Instead of random initialization, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual.
ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.
# License: BSD
# Author: Sasank Chilamkurthy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
from PIL import Image
from tempfile import TemporaryDirectory
cudnn.benchmark = True
plt.ion() # interactive mode
<contextlib.ExitStack object at 0x7f56d281ce20>
Load Data#
We will use torchvision and torch.utils.data packages for loading the data.
The problem we’re going to solve today is to train a model to classify ants and bees. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well.
This dataset is a very small subset of imagenet.
Note
Download the data from here and extract it to the current directory.
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")
Using cuda device
Visualize a few images#
Let’s visualize a few training images so as to understand the data augmentations.
def imshow(inp, title=None):
"""Display image for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))
# Make a grid from batch
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
![['bees', 'bees', 'ants', 'ants']](../_images/sphx_glr_transfer_learning_tutorial_001.png)
Training the model#
Now, let’s write a general function to train a model. Here, we will illustrate:
Scheduling the learning rate
Saving the best model
In the following, parameter scheduler is an LR scheduler object from
torch.optim.lr_scheduler.
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
# Create a temporary directory to save training checkpoints
with TemporaryDirectory() as tempdir:
best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')
torch.save(model.state_dict(), best_model_params_path)
best_acc = 0.0
for epoch in range(num_epochs):
print(f'Epoch {epoch}/{num_epochs - 1}')
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
torch.save(model.state_dict(), best_model_params_path)
print()
time_elapsed = time.time() - since
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best val Acc: {best_acc:4f}')
# load best model weights
model.load_state_dict(torch.load(best_model_params_path, weights_only=True))
return model
Visualizing the model predictions#
Generic function to display predictions for a few images
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title(f'predicted: {class_names[preds[j]]}')
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
Finetuning the ConvNet#
Load a pretrained model and reset final fully connected layer.
model_ft = models.resnet18(weights='IMAGENET1K_V1')
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to ``nn.Linear(num_ftrs, len(class_names))``.
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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Train and evaluate#
It should take around 15-25 min on CPU. On GPU though, it takes less than a minute.
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
Epoch 0/24
----------
train Loss: 0.6247 Acc: 0.6598
val Loss: 0.2423 Acc: 0.9150
Epoch 1/24
----------
train Loss: 0.5729 Acc: 0.7787
val Loss: 0.2898 Acc: 0.8889
Epoch 2/24
----------
train Loss: 0.5971 Acc: 0.7541
val Loss: 0.3867 Acc: 0.8431
Epoch 3/24
----------
train Loss: 0.4133 Acc: 0.8484
val Loss: 0.4098 Acc: 0.8693
Epoch 4/24
----------
train Loss: 0.5296 Acc: 0.8033
val Loss: 0.2939 Acc: 0.8954
Epoch 5/24
----------
train Loss: 0.5064 Acc: 0.7992
val Loss: 0.2182 Acc: 0.9346
Epoch 6/24
----------
train Loss: 0.5469 Acc: 0.7869
val Loss: 0.3466 Acc: 0.8954
Epoch 7/24
----------
train Loss: 0.3554 Acc: 0.8443
val Loss: 0.2485 Acc: 0.9085
Epoch 8/24
----------
train Loss: 0.3578 Acc: 0.8484
val Loss: 0.2616 Acc: 0.9020
Epoch 9/24
----------
train Loss: 0.2425 Acc: 0.9016
val Loss: 0.2425 Acc: 0.9216
Epoch 10/24
----------
train Loss: 0.3278 Acc: 0.8566
val Loss: 0.2496 Acc: 0.8954
Epoch 11/24
----------
train Loss: 0.2179 Acc: 0.8934
val Loss: 0.2238 Acc: 0.9216
Epoch 12/24
----------
train Loss: 0.2275 Acc: 0.9098
val Loss: 0.2138 Acc: 0.9150
Epoch 13/24
----------
train Loss: 0.3389 Acc: 0.8443
val Loss: 0.2364 Acc: 0.9216
Epoch 14/24
----------
train Loss: 0.2949 Acc: 0.8689
val Loss: 0.2140 Acc: 0.9346
Epoch 15/24
----------
train Loss: 0.3235 Acc: 0.8607
val Loss: 0.2314 Acc: 0.9216
Epoch 16/24
----------
train Loss: 0.2737 Acc: 0.8648
val Loss: 0.2404 Acc: 0.9150
Epoch 17/24
----------
train Loss: 0.3643 Acc: 0.8361
val Loss: 0.2446 Acc: 0.9085
Epoch 18/24
----------
train Loss: 0.2712 Acc: 0.9098
val Loss: 0.2478 Acc: 0.9216
Epoch 19/24
----------
train Loss: 0.2355 Acc: 0.9098
val Loss: 0.2242 Acc: 0.9216
Epoch 20/24
----------
train Loss: 0.2304 Acc: 0.9098
val Loss: 0.2217 Acc: 0.9281
Epoch 21/24
----------
train Loss: 0.2872 Acc: 0.8770
val Loss: 0.2098 Acc: 0.9281
Epoch 22/24
----------
train Loss: 0.2353 Acc: 0.9057
val Loss: 0.2248 Acc: 0.9346
Epoch 23/24
----------
train Loss: 0.3088 Acc: 0.8852
val Loss: 0.2360 Acc: 0.9281
Epoch 24/24
----------
train Loss: 0.2412 Acc: 0.8975
val Loss: 0.2326 Acc: 0.9216
Training complete in 0m 34s
Best val Acc: 0.934641
visualize_model(model_ft)
ConvNet as fixed feature extractor#
Here, we need to freeze all the network except the final layer. We need
to set requires_grad = False to freeze the parameters so that the
gradients are not computed in backward().
You can read more about this in the documentation here.
model_conv = torchvision.models.resnet18(weights='IMAGENET1K_V1')
for param in model_conv.parameters():
param.requires_grad = False
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)
model_conv = model_conv.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that only parameters of final layer are being optimized as
# opposed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
Train and evaluate#
On CPU this will take about half the time compared to previous scenario. This is expected as gradients don’t need to be computed for most of the network. However, forward does need to be computed.
model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)
Epoch 0/24
----------
train Loss: 0.5700 Acc: 0.6803
val Loss: 0.2496 Acc: 0.9085
Epoch 1/24
----------
train Loss: 0.4876 Acc: 0.7459
val Loss: 0.2403 Acc: 0.9085
Epoch 2/24
----------
train Loss: 0.5543 Acc: 0.7582
val Loss: 0.3171 Acc: 0.8758
Epoch 3/24
----------
train Loss: 0.5721 Acc: 0.7705
val Loss: 0.3772 Acc: 0.8497
Epoch 4/24
----------
train Loss: 0.3931 Acc: 0.8566
val Loss: 0.2478 Acc: 0.9150
Epoch 5/24
----------
train Loss: 0.4475 Acc: 0.7951
val Loss: 0.3610 Acc: 0.8301
Epoch 6/24
----------
train Loss: 0.5722 Acc: 0.7869
val Loss: 0.3631 Acc: 0.8693
Epoch 7/24
----------
train Loss: 0.5449 Acc: 0.7910
val Loss: 0.1731 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.4526 Acc: 0.7910
val Loss: 0.1942 Acc: 0.9412
Epoch 9/24
----------
train Loss: 0.3652 Acc: 0.8238
val Loss: 0.1845 Acc: 0.9412
Epoch 10/24
----------
train Loss: 0.3991 Acc: 0.8156
val Loss: 0.1815 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.2739 Acc: 0.8770
val Loss: 0.1979 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.3980 Acc: 0.7951
val Loss: 0.1854 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.3877 Acc: 0.8443
val Loss: 0.1999 Acc: 0.9412
Epoch 14/24
----------
train Loss: 0.3250 Acc: 0.8279
val Loss: 0.1906 Acc: 0.9412
Epoch 15/24
----------
train Loss: 0.3235 Acc: 0.8361
val Loss: 0.1893 Acc: 0.9477
Epoch 16/24
----------
train Loss: 0.4239 Acc: 0.8197
val Loss: 0.1905 Acc: 0.9477
Epoch 17/24
----------
train Loss: 0.2901 Acc: 0.8730
val Loss: 0.2052 Acc: 0.9412
Epoch 18/24
----------
train Loss: 0.2952 Acc: 0.8525
val Loss: 0.2126 Acc: 0.9216
Epoch 19/24
----------
train Loss: 0.3656 Acc: 0.8443
val Loss: 0.1983 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.4072 Acc: 0.8033
val Loss: 0.2004 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.2549 Acc: 0.8975
val Loss: 0.2122 Acc: 0.9346
Epoch 22/24
----------
train Loss: 0.3480 Acc: 0.8443
val Loss: 0.1837 Acc: 0.9477
Epoch 23/24
----------
train Loss: 0.3858 Acc: 0.8279
val Loss: 0.2096 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.3164 Acc: 0.8689
val Loss: 0.1835 Acc: 0.9477
Training complete in 0m 28s
Best val Acc: 0.947712
visualize_model(model_conv)
plt.ioff()
plt.show()
Inference on custom images#
Use the trained model to make predictions on custom images and visualize the predicted class labels along with the images.
def visualize_model_predictions(model,img_path):
was_training = model.training
model.eval()
img = Image.open(img_path)
img = data_transforms['val'](img)
img = img.unsqueeze(0)
img = img.to(device)
with torch.no_grad():
outputs = model(img)
_, preds = torch.max(outputs, 1)
ax = plt.subplot(2,2,1)
ax.axis('off')
ax.set_title(f'Predicted: {class_names[preds[0]]}')
imshow(img.cpu().data[0])
model.train(mode=was_training)
visualize_model_predictions(
model_conv,
img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg'
)
plt.ioff()
plt.show()
Further Learning#
If you would like to learn more about the applications of transfer learning, checkout our Quantized Transfer Learning for Computer Vision Tutorial.
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