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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 0x7f0f20374d30>
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', '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
0%| | 0.00/44.7M [00:00<?, ?B/s]
88%|████████▊ | 39.2M/44.7M [00:00<00:00, 411MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 412MB/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: 0.6305 Acc: 0.6885
val Loss: 0.2659 Acc: 0.9020
Epoch 1/24
----------
train Loss: 0.4752 Acc: 0.7951
val Loss: 0.4969 Acc: 0.8497
Epoch 2/24
----------
train Loss: 0.5602 Acc: 0.7582
val Loss: 0.2677 Acc: 0.8954
Epoch 3/24
----------
train Loss: 0.4387 Acc: 0.8197
val Loss: 0.3235 Acc: 0.8758
Epoch 4/24
----------
train Loss: 0.5597 Acc: 0.8115
val Loss: 0.2076 Acc: 0.8954
Epoch 5/24
----------
train Loss: 0.4715 Acc: 0.8074
val Loss: 0.5180 Acc: 0.8497
Epoch 6/24
----------
train Loss: 0.4769 Acc: 0.8115
val Loss: 0.2044 Acc: 0.9085
Epoch 7/24
----------
train Loss: 0.3316 Acc: 0.8689
val Loss: 0.1739 Acc: 0.9085
Epoch 8/24
----------
train Loss: 0.3079 Acc: 0.8852
val Loss: 0.1572 Acc: 0.9216
Epoch 9/24
----------
train Loss: 0.3212 Acc: 0.8689
val Loss: 0.1726 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3342 Acc: 0.8607
val Loss: 0.1784 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.2628 Acc: 0.8893
val Loss: 0.1542 Acc: 0.9085
Epoch 12/24
----------
train Loss: 0.3362 Acc: 0.8197
val Loss: 0.2098 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.3478 Acc: 0.8525
val Loss: 0.1731 Acc: 0.9150
Epoch 14/24
----------
train Loss: 0.3051 Acc: 0.8607
val Loss: 0.1793 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.3556 Acc: 0.8484
val Loss: 0.1685 Acc: 0.9281
Epoch 16/24
----------
train Loss: 0.3073 Acc: 0.8730
val Loss: 0.1685 Acc: 0.9412
Epoch 17/24
----------
train Loss: 0.2779 Acc: 0.8770
val Loss: 0.1954 Acc: 0.9346
Epoch 18/24
----------
train Loss: 0.2506 Acc: 0.8975
val Loss: 0.1765 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.2781 Acc: 0.8893
val Loss: 0.1667 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.2906 Acc: 0.8689
val Loss: 0.1751 Acc: 0.9150
Epoch 21/24
----------
train Loss: 0.3131 Acc: 0.8443
val Loss: 0.1759 Acc: 0.9346
Epoch 22/24
----------
train Loss: 0.2044 Acc: 0.9139
val Loss: 0.1716 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.2454 Acc: 0.9057
val Loss: 0.1793 Acc: 0.9346
Epoch 24/24
----------
train Loss: 0.2545 Acc: 0.8893
val Loss: 0.1977 Acc: 0.9346
Training complete in 0m 36s
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: 0.6563 Acc: 0.6639
val Loss: 0.3022 Acc: 0.8431
Epoch 1/24
----------
train Loss: 0.4356 Acc: 0.7828
val Loss: 0.1661 Acc: 0.9477
Epoch 2/24
----------
train Loss: 0.3627 Acc: 0.8443
val Loss: 0.2766 Acc: 0.8693
Epoch 3/24
----------
train Loss: 0.4006 Acc: 0.8156
val Loss: 0.4283 Acc: 0.8301
Epoch 4/24
----------
train Loss: 0.4035 Acc: 0.8361
val Loss: 0.1785 Acc: 0.9412
Epoch 5/24
----------
train Loss: 0.5064 Acc: 0.7910
val Loss: 0.2292 Acc: 0.9216
Epoch 6/24
----------
train Loss: 0.5047 Acc: 0.7746
val Loss: 0.1622 Acc: 0.9542
Epoch 7/24
----------
train Loss: 0.3145 Acc: 0.8730
val Loss: 0.1650 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.3276 Acc: 0.8566
val Loss: 0.1649 Acc: 0.9542
Epoch 9/24
----------
train Loss: 0.4291 Acc: 0.8074
val Loss: 0.1703 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3920 Acc: 0.8320
val Loss: 0.1678 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.3214 Acc: 0.8689
val Loss: 0.1630 Acc: 0.9542
Epoch 12/24
----------
train Loss: 0.3797 Acc: 0.8197
val Loss: 0.1648 Acc: 0.9542
Epoch 13/24
----------
train Loss: 0.4039 Acc: 0.8115
val Loss: 0.1933 Acc: 0.9412
Epoch 14/24
----------
train Loss: 0.3494 Acc: 0.8607
val Loss: 0.1683 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3530 Acc: 0.8443
val Loss: 0.1607 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.2990 Acc: 0.8893
val Loss: 0.2221 Acc: 0.9216
Epoch 17/24
----------
train Loss: 0.3259 Acc: 0.8484
val Loss: 0.1715 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.2664 Acc: 0.8566
val Loss: 0.1654 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.3011 Acc: 0.8566
val Loss: 0.1646 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.3602 Acc: 0.8361
val Loss: 0.1970 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3167 Acc: 0.8566
val Loss: 0.1772 Acc: 0.9477
Epoch 22/24
----------
train Loss: 0.3844 Acc: 0.8361
val Loss: 0.1821 Acc: 0.9412
Epoch 23/24
----------
train Loss: 0.2875 Acc: 0.8484
val Loss: 0.1736 Acc: 0.9477
Epoch 24/24
----------
train Loss: 0.3389 Acc: 0.8566
val Loss: 0.1883 Acc: 0.9477
Training complete in 0m 28s
Best val Acc: 0.954248
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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