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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])
![['ants', 'ants', 'bees', '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
0%| | 0.00/44.7M [00:00<?, ?B/s]
86%|████████▌ | 38.2M/44.7M [00:00<00:00, 401MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 405MB/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: 0.6373 Acc: 0.6516
val Loss: 0.2572 Acc: 0.9085
Epoch 1/24
----------
train Loss: 0.5740 Acc: 0.7623
val Loss: 0.2817 Acc: 0.8824
Epoch 2/24
----------
train Loss: 0.6096 Acc: 0.7992
val Loss: 0.3793 Acc: 0.8562
Epoch 3/24
----------
train Loss: 0.5331 Acc: 0.7828
val Loss: 0.5794 Acc: 0.7778
Epoch 4/24
----------
train Loss: 0.4826 Acc: 0.7992
val Loss: 0.2825 Acc: 0.8693
Epoch 5/24
----------
train Loss: 0.4198 Acc: 0.8443
val Loss: 0.3402 Acc: 0.8497
Epoch 6/24
----------
train Loss: 0.5800 Acc: 0.7992
val Loss: 0.3691 Acc: 0.8627
Epoch 7/24
----------
train Loss: 0.4223 Acc: 0.8238
val Loss: 0.2912 Acc: 0.9020
Epoch 8/24
----------
train Loss: 0.3087 Acc: 0.8607
val Loss: 0.3402 Acc: 0.8497
Epoch 9/24
----------
train Loss: 0.3541 Acc: 0.8484
val Loss: 0.3100 Acc: 0.9020
Epoch 10/24
----------
train Loss: 0.3052 Acc: 0.8443
val Loss: 0.2970 Acc: 0.9020
Epoch 11/24
----------
train Loss: 0.3013 Acc: 0.8648
val Loss: 0.2608 Acc: 0.9216
Epoch 12/24
----------
train Loss: 0.3507 Acc: 0.8607
val Loss: 0.2229 Acc: 0.9216
Epoch 13/24
----------
train Loss: 0.2282 Acc: 0.8852
val Loss: 0.2335 Acc: 0.9150
Epoch 14/24
----------
train Loss: 0.2732 Acc: 0.8811
val Loss: 0.2603 Acc: 0.8954
Epoch 15/24
----------
train Loss: 0.2815 Acc: 0.8934
val Loss: 0.2593 Acc: 0.8954
Epoch 16/24
----------
train Loss: 0.3011 Acc: 0.8648
val Loss: 0.2444 Acc: 0.9020
Epoch 17/24
----------
train Loss: 0.2845 Acc: 0.8648
val Loss: 0.2330 Acc: 0.9150
Epoch 18/24
----------
train Loss: 0.2321 Acc: 0.9057
val Loss: 0.2444 Acc: 0.9085
Epoch 19/24
----------
train Loss: 0.2707 Acc: 0.8730
val Loss: 0.2563 Acc: 0.9020
Epoch 20/24
----------
train Loss: 0.3090 Acc: 0.8852
val Loss: 0.2208 Acc: 0.9216
Epoch 21/24
----------
train Loss: 0.3351 Acc: 0.8566
val Loss: 0.2936 Acc: 0.8954
Epoch 22/24
----------
train Loss: 0.2792 Acc: 0.8770
val Loss: 0.2424 Acc: 0.9020
Epoch 23/24
----------
train Loss: 0.2974 Acc: 0.8648
val Loss: 0.2344 Acc: 0.9085
Epoch 24/24
----------
train Loss: 0.3274 Acc: 0.8689
val Loss: 0.2757 Acc: 0.8824
Training complete in 0m 36s
Best val Acc: 0.921569
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: 0.6005 Acc: 0.6516
val Loss: 0.3484 Acc: 0.8497
Epoch 1/24
----------
train Loss: 0.4055 Acc: 0.8074
val Loss: 0.1546 Acc: 0.9477
Epoch 2/24
----------
train Loss: 0.3788 Acc: 0.8238
val Loss: 0.2740 Acc: 0.8889
Epoch 3/24
----------
train Loss: 0.4735 Acc: 0.8074
val Loss: 0.1895 Acc: 0.9346
Epoch 4/24
----------
train Loss: 0.4034 Acc: 0.8402
val Loss: 0.1625 Acc: 0.9346
Epoch 5/24
----------
train Loss: 0.3522 Acc: 0.8443
val Loss: 0.1662 Acc: 0.9542
Epoch 6/24
----------
train Loss: 0.5148 Acc: 0.7664
val Loss: 0.5580 Acc: 0.7974
Epoch 7/24
----------
train Loss: 0.5086 Acc: 0.7910
val Loss: 0.1755 Acc: 0.9542
Epoch 8/24
----------
train Loss: 0.3373 Acc: 0.8279
val Loss: 0.2159 Acc: 0.9412
Epoch 9/24
----------
train Loss: 0.3281 Acc: 0.8525
val Loss: 0.1755 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3662 Acc: 0.8648
val Loss: 0.1813 Acc: 0.9542
Epoch 11/24
----------
train Loss: 0.3764 Acc: 0.8279
val Loss: 0.1766 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.3467 Acc: 0.8361
val Loss: 0.2039 Acc: 0.9412
Epoch 13/24
----------
train Loss: 0.2729 Acc: 0.8730
val Loss: 0.1693 Acc: 0.9542
Epoch 14/24
----------
train Loss: 0.4085 Acc: 0.8156
val Loss: 0.1752 Acc: 0.9542
Epoch 15/24
----------
train Loss: 0.3271 Acc: 0.8852
val Loss: 0.1763 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.3405 Acc: 0.8402
val Loss: 0.1858 Acc: 0.9608
Epoch 17/24
----------
train Loss: 0.2761 Acc: 0.9016
val Loss: 0.1821 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.3294 Acc: 0.8320
val Loss: 0.2104 Acc: 0.9412
Epoch 19/24
----------
train Loss: 0.2609 Acc: 0.8893
val Loss: 0.1764 Acc: 0.9542
Epoch 20/24
----------
train Loss: 0.3810 Acc: 0.8443
val Loss: 0.1840 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3185 Acc: 0.8525
val Loss: 0.1795 Acc: 0.9477
Epoch 22/24
----------
train Loss: 0.3281 Acc: 0.8811
val Loss: 0.2054 Acc: 0.9412
Epoch 23/24
----------
train Loss: 0.3509 Acc: 0.8443
val Loss: 0.2059 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.3028 Acc: 0.8443
val Loss: 0.1932 Acc: 0.9542
Training complete in 0m 28s
Best val Acc: 0.960784
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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