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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', '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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92%|█████████▏| 41.0M/44.7M [00:00<00:00, 429MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 430MB/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: 0.5693 Acc: 0.6885
val Loss: 0.2175 Acc: 0.9020
Epoch 1/24
----------
train Loss: 0.4666 Acc: 0.7910
val Loss: 0.3241 Acc: 0.8758
Epoch 2/24
----------
train Loss: 0.3519 Acc: 0.8402
val Loss: 0.1995 Acc: 0.9346
Epoch 3/24
----------
train Loss: 0.4874 Acc: 0.8443
val Loss: 0.5057 Acc: 0.8105
Epoch 4/24
----------
train Loss: 0.6983 Acc: 0.7746
val Loss: 0.4558 Acc: 0.8170
Epoch 5/24
----------
train Loss: 0.6744 Acc: 0.7459
val Loss: 0.3205 Acc: 0.8824
Epoch 6/24
----------
train Loss: 0.3319 Acc: 0.8689
val Loss: 0.3004 Acc: 0.8954
Epoch 7/24
----------
train Loss: 0.3104 Acc: 0.8443
val Loss: 0.3053 Acc: 0.8824
Epoch 8/24
----------
train Loss: 0.3374 Acc: 0.8402
val Loss: 0.2488 Acc: 0.9150
Epoch 9/24
----------
train Loss: 0.3142 Acc: 0.8648
val Loss: 0.2842 Acc: 0.9020
Epoch 10/24
----------
train Loss: 0.2133 Acc: 0.9303
val Loss: 0.2528 Acc: 0.9020
Epoch 11/24
----------
train Loss: 0.2632 Acc: 0.8975
val Loss: 0.2448 Acc: 0.9085
Epoch 12/24
----------
train Loss: 0.3113 Acc: 0.8484
val Loss: 0.2580 Acc: 0.9085
Epoch 13/24
----------
train Loss: 0.3404 Acc: 0.8361
val Loss: 0.2463 Acc: 0.9085
Epoch 14/24
----------
train Loss: 0.2342 Acc: 0.9057
val Loss: 0.2371 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.3444 Acc: 0.8525
val Loss: 0.2537 Acc: 0.9085
Epoch 16/24
----------
train Loss: 0.1913 Acc: 0.9303
val Loss: 0.2465 Acc: 0.9216
Epoch 17/24
----------
train Loss: 0.2708 Acc: 0.8607
val Loss: 0.2527 Acc: 0.9085
Epoch 18/24
----------
train Loss: 0.2856 Acc: 0.8770
val Loss: 0.2427 Acc: 0.9150
Epoch 19/24
----------
train Loss: 0.2042 Acc: 0.9180
val Loss: 0.2254 Acc: 0.9281
Epoch 20/24
----------
train Loss: 0.3386 Acc: 0.8361
val Loss: 0.2629 Acc: 0.9150
Epoch 21/24
----------
train Loss: 0.3291 Acc: 0.8607
val Loss: 0.2453 Acc: 0.9085
Epoch 22/24
----------
train Loss: 0.3015 Acc: 0.8566
val Loss: 0.2514 Acc: 0.9150
Epoch 23/24
----------
train Loss: 0.2167 Acc: 0.8934
val Loss: 0.2531 Acc: 0.9085
Epoch 24/24
----------
train Loss: 0.3220 Acc: 0.8607
val Loss: 0.2493 Acc: 0.9085
Training complete in 0m 35s
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: 0.5775 Acc: 0.6762
val Loss: 0.2536 Acc: 0.9216
Epoch 1/24
----------
train Loss: 0.4387 Acc: 0.8115
val Loss: 0.1895 Acc: 0.9477
Epoch 2/24
----------
train Loss: 0.4539 Acc: 0.7828
val Loss: 0.1979 Acc: 0.9281
Epoch 3/24
----------
train Loss: 0.3295 Acc: 0.8484
val Loss: 0.1803 Acc: 0.9477
Epoch 4/24
----------
train Loss: 0.6746 Acc: 0.7541
val Loss: 0.4073 Acc: 0.8758
Epoch 5/24
----------
train Loss: 0.8013 Acc: 0.7459
val Loss: 0.3447 Acc: 0.8954
Epoch 6/24
----------
train Loss: 0.5228 Acc: 0.7787
val Loss: 0.2257 Acc: 0.9216
Epoch 7/24
----------
train Loss: 0.4639 Acc: 0.8279
val Loss: 0.1828 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.4518 Acc: 0.8115
val Loss: 0.1911 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.3661 Acc: 0.8443
val Loss: 0.1939 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.4015 Acc: 0.8566
val Loss: 0.2145 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.4871 Acc: 0.7992
val Loss: 0.1970 Acc: 0.9542
Epoch 12/24
----------
train Loss: 0.3718 Acc: 0.8648
val Loss: 0.1785 Acc: 0.9608
Epoch 13/24
----------
train Loss: 0.3478 Acc: 0.8648
val Loss: 0.1888 Acc: 0.9477
Epoch 14/24
----------
train Loss: 0.3402 Acc: 0.8402
val Loss: 0.1937 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3698 Acc: 0.8279
val Loss: 0.2453 Acc: 0.9346
Epoch 16/24
----------
train Loss: 0.2973 Acc: 0.8852
val Loss: 0.1835 Acc: 0.9542
Epoch 17/24
----------
train Loss: 0.2601 Acc: 0.9016
val Loss: 0.1733 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.2958 Acc: 0.8730
val Loss: 0.1893 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.3787 Acc: 0.8320
val Loss: 0.1785 Acc: 0.9542
Epoch 20/24
----------
train Loss: 0.4097 Acc: 0.8320
val Loss: 0.1916 Acc: 0.9542
Epoch 21/24
----------
train Loss: 0.3238 Acc: 0.8607
val Loss: 0.2158 Acc: 0.9412
Epoch 22/24
----------
train Loss: 0.3606 Acc: 0.8320
val Loss: 0.2110 Acc: 0.9412
Epoch 23/24
----------
train Loss: 0.3601 Acc: 0.8238
val Loss: 0.2081 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.3019 Acc: 0.8811
val Loss: 0.1917 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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