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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', 'bees', '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
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100%|██████████| 44.7M/44.7M [00:00<00:00, 437MB/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: 0.5648 Acc: 0.6967
val Loss: 0.2226 Acc: 0.9281
Epoch 1/24
----------
train Loss: 0.5294 Acc: 0.7992
val Loss: 0.3332 Acc: 0.9020
Epoch 2/24
----------
train Loss: 0.5515 Acc: 0.8074
val Loss: 0.2276 Acc: 0.9150
Epoch 3/24
----------
train Loss: 0.4028 Acc: 0.8443
val Loss: 0.4507 Acc: 0.8366
Epoch 4/24
----------
train Loss: 0.2888 Acc: 0.8648
val Loss: 0.3781 Acc: 0.8627
Epoch 5/24
----------
train Loss: 0.4533 Acc: 0.8320
val Loss: 0.3085 Acc: 0.8889
Epoch 6/24
----------
train Loss: 0.4936 Acc: 0.8320
val Loss: 0.3898 Acc: 0.8693
Epoch 7/24
----------
train Loss: 0.5059 Acc: 0.8156
val Loss: 0.2828 Acc: 0.8889
Epoch 8/24
----------
train Loss: 0.3300 Acc: 0.8648
val Loss: 0.2147 Acc: 0.9216
Epoch 9/24
----------
train Loss: 0.3379 Acc: 0.8443
val Loss: 0.2158 Acc: 0.9085
Epoch 10/24
----------
train Loss: 0.2791 Acc: 0.8811
val Loss: 0.2051 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.2675 Acc: 0.8975
val Loss: 0.2569 Acc: 0.8889
Epoch 12/24
----------
train Loss: 0.2177 Acc: 0.9262
val Loss: 0.2049 Acc: 0.9216
Epoch 13/24
----------
train Loss: 0.2679 Acc: 0.8975
val Loss: 0.2329 Acc: 0.8954
Epoch 14/24
----------
train Loss: 0.2756 Acc: 0.8852
val Loss: 0.2273 Acc: 0.9085
Epoch 15/24
----------
train Loss: 0.3063 Acc: 0.8402
val Loss: 0.2113 Acc: 0.9020
Epoch 16/24
----------
train Loss: 0.3063 Acc: 0.8484
val Loss: 0.2192 Acc: 0.9020
Epoch 17/24
----------
train Loss: 0.2620 Acc: 0.8648
val Loss: 0.1920 Acc: 0.9216
Epoch 18/24
----------
train Loss: 0.2625 Acc: 0.8893
val Loss: 0.1967 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.2690 Acc: 0.8893
val Loss: 0.2057 Acc: 0.9085
Epoch 20/24
----------
train Loss: 0.2255 Acc: 0.9180
val Loss: 0.2251 Acc: 0.9020
Epoch 21/24
----------
train Loss: 0.1930 Acc: 0.9303
val Loss: 0.2245 Acc: 0.9216
Epoch 22/24
----------
train Loss: 0.2255 Acc: 0.8975
val Loss: 0.2160 Acc: 0.9020
Epoch 23/24
----------
train Loss: 0.2603 Acc: 0.8975
val Loss: 0.2361 Acc: 0.9020
Epoch 24/24
----------
train Loss: 0.2349 Acc: 0.8934
val Loss: 0.1968 Acc: 0.9281
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: 0.6167 Acc: 0.6311
val Loss: 0.4433 Acc: 0.7190
Epoch 1/24
----------
train Loss: 0.5248 Acc: 0.7541
val Loss: 0.1946 Acc: 0.9412
Epoch 2/24
----------
train Loss: 0.4508 Acc: 0.8033
val Loss: 0.1722 Acc: 0.9412
Epoch 3/24
----------
train Loss: 0.4292 Acc: 0.8156
val Loss: 0.1677 Acc: 0.9608
Epoch 4/24
----------
train Loss: 0.4446 Acc: 0.8238
val Loss: 0.2325 Acc: 0.8954
Epoch 5/24
----------
train Loss: 0.4754 Acc: 0.8156
val Loss: 0.1803 Acc: 0.9412
Epoch 6/24
----------
train Loss: 0.4880 Acc: 0.7623
val Loss: 0.2852 Acc: 0.8824
Epoch 7/24
----------
train Loss: 0.3499 Acc: 0.8648
val Loss: 0.1800 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.3325 Acc: 0.8566
val Loss: 0.1675 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.3002 Acc: 0.8443
val Loss: 0.1495 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3023 Acc: 0.8811
val Loss: 0.1503 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.3425 Acc: 0.8525
val Loss: 0.1622 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.3073 Acc: 0.8770
val Loss: 0.1606 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.3241 Acc: 0.8689
val Loss: 0.1830 Acc: 0.9412
Epoch 14/24
----------
train Loss: 0.3479 Acc: 0.8525
val Loss: 0.1909 Acc: 0.9412
Epoch 15/24
----------
train Loss: 0.3204 Acc: 0.8484
val Loss: 0.1423 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.4086 Acc: 0.7951
val Loss: 0.1612 Acc: 0.9477
Epoch 17/24
----------
train Loss: 0.2844 Acc: 0.8811
val Loss: 0.1544 Acc: 0.9477
Epoch 18/24
----------
train Loss: 0.3286 Acc: 0.8443
val Loss: 0.1699 Acc: 0.9412
Epoch 19/24
----------
train Loss: 0.4047 Acc: 0.8197
val Loss: 0.1524 Acc: 0.9542
Epoch 20/24
----------
train Loss: 0.3154 Acc: 0.8566
val Loss: 0.1478 Acc: 0.9542
Epoch 21/24
----------
train Loss: 0.3327 Acc: 0.8361
val Loss: 0.1698 Acc: 0.9477
Epoch 22/24
----------
train Loss: 0.2747 Acc: 0.8852
val Loss: 0.1534 Acc: 0.9542
Epoch 23/24
----------
train Loss: 0.3651 Acc: 0.8279
val Loss: 0.1695 Acc: 0.9477
Epoch 24/24
----------
train Loss: 0.2864 Acc: 0.8648
val Loss: 0.1494 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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