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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
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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', 'bees', 'bees']](../_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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82%|████████▏ | 36.8M/44.7M [00:00<00:00, 385MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 392MB/s]
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: 0.5205 Acc: 0.7459
val Loss: 0.2264 Acc: 0.9346
Epoch 1/24
----------
train Loss: 0.6626 Acc: 0.7664
val Loss: 0.1903 Acc: 0.9412
Epoch 2/24
----------
train Loss: 0.5609 Acc: 0.7787
val Loss: 0.3774 Acc: 0.8758
Epoch 3/24
----------
train Loss: 0.7814 Acc: 0.7500
val Loss: 0.2727 Acc: 0.9020
Epoch 4/24
----------
train Loss: 0.6930 Acc: 0.7459
val Loss: 0.3126 Acc: 0.9216
Epoch 5/24
----------
train Loss: 0.4284 Acc: 0.7951
val Loss: 0.5403 Acc: 0.8497
Epoch 6/24
----------
train Loss: 0.5188 Acc: 0.8033
val Loss: 0.2928 Acc: 0.9085
Epoch 7/24
----------
train Loss: 0.3480 Acc: 0.8525
val Loss: 0.2384 Acc: 0.9216
Epoch 8/24
----------
train Loss: 0.2616 Acc: 0.8934
val Loss: 0.2462 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.2707 Acc: 0.8689
val Loss: 0.2243 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3472 Acc: 0.8525
val Loss: 0.2290 Acc: 0.9281
Epoch 11/24
----------
train Loss: 0.3364 Acc: 0.8525
val Loss: 0.2171 Acc: 0.9346
Epoch 12/24
----------
train Loss: 0.2545 Acc: 0.8934
val Loss: 0.2192 Acc: 0.9412
Epoch 13/24
----------
train Loss: 0.2924 Acc: 0.8566
val Loss: 0.2536 Acc: 0.9281
Epoch 14/24
----------
train Loss: 0.3376 Acc: 0.8525
val Loss: 0.2309 Acc: 0.9412
Epoch 15/24
----------
train Loss: 0.2256 Acc: 0.9098
val Loss: 0.2390 Acc: 0.9216
Epoch 16/24
----------
train Loss: 0.3108 Acc: 0.8525
val Loss: 0.2411 Acc: 0.9346
Epoch 17/24
----------
train Loss: 0.2519 Acc: 0.8893
val Loss: 0.2426 Acc: 0.9281
Epoch 18/24
----------
train Loss: 0.2091 Acc: 0.9262
val Loss: 0.2414 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.1760 Acc: 0.9385
val Loss: 0.2335 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.2554 Acc: 0.9057
val Loss: 0.2307 Acc: 0.9412
Epoch 21/24
----------
train Loss: 0.2806 Acc: 0.8811
val Loss: 0.2672 Acc: 0.9216
Epoch 22/24
----------
train Loss: 0.2358 Acc: 0.9221
val Loss: 0.2293 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.3191 Acc: 0.8730
val Loss: 0.2418 Acc: 0.9216
Epoch 24/24
----------
train Loss: 0.1808 Acc: 0.9221
val Loss: 0.2293 Acc: 0.9346
Training complete in 0m 37s
Best val Acc: 0.947712
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: 0.6425 Acc: 0.6557
val Loss: 0.2534 Acc: 0.9216
Epoch 1/24
----------
train Loss: 0.4432 Acc: 0.7910
val Loss: 0.2115 Acc: 0.9346
Epoch 2/24
----------
train Loss: 0.4601 Acc: 0.7910
val Loss: 0.2607 Acc: 0.9085
Epoch 3/24
----------
train Loss: 0.4369 Acc: 0.8115
val Loss: 0.1869 Acc: 0.9477
Epoch 4/24
----------
train Loss: 0.5486 Acc: 0.7705
val Loss: 0.1862 Acc: 0.9477
Epoch 5/24
----------
train Loss: 0.5443 Acc: 0.7582
val Loss: 0.1941 Acc: 0.9346
Epoch 6/24
----------
train Loss: 0.4544 Acc: 0.8238
val Loss: 0.2137 Acc: 0.9281
Epoch 7/24
----------
train Loss: 0.3578 Acc: 0.8484
val Loss: 0.1795 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.3237 Acc: 0.8607
val Loss: 0.2263 Acc: 0.9216
Epoch 9/24
----------
train Loss: 0.3785 Acc: 0.8197
val Loss: 0.2008 Acc: 0.9281
Epoch 10/24
----------
train Loss: 0.4293 Acc: 0.8033
val Loss: 0.1791 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.3794 Acc: 0.8115
val Loss: 0.1876 Acc: 0.9412
Epoch 12/24
----------
train Loss: 0.3922 Acc: 0.7951
val Loss: 0.1922 Acc: 0.9281
Epoch 13/24
----------
train Loss: 0.4501 Acc: 0.7910
val Loss: 0.1799 Acc: 0.9412
Epoch 14/24
----------
train Loss: 0.3923 Acc: 0.8279
val Loss: 0.1772 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3802 Acc: 0.7992
val Loss: 0.1963 Acc: 0.9412
Epoch 16/24
----------
train Loss: 0.3290 Acc: 0.8484
val Loss: 0.1803 Acc: 0.9346
Epoch 17/24
----------
train Loss: 0.3138 Acc: 0.8607
val Loss: 0.1835 Acc: 0.9412
Epoch 18/24
----------
train Loss: 0.3473 Acc: 0.8648
val Loss: 0.2228 Acc: 0.9216
Epoch 19/24
----------
train Loss: 0.3194 Acc: 0.8893
val Loss: 0.2135 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.3600 Acc: 0.8525
val Loss: 0.1900 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3843 Acc: 0.8443
val Loss: 0.2042 Acc: 0.9281
Epoch 22/24
----------
train Loss: 0.3638 Acc: 0.8443
val Loss: 0.1995 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.2802 Acc: 0.8770
val Loss: 0.2017 Acc: 0.9346
Epoch 24/24
----------
train Loss: 0.4518 Acc: 0.8156
val Loss: 0.2125 Acc: 0.9216
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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