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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', '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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95%|█████████▍| 42.2M/44.7M [00:00<00:00, 442MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 441MB/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: 0.5026 Acc: 0.7500
val Loss: 0.2449 Acc: 0.8954
Epoch 1/24
----------
train Loss: 0.4284 Acc: 0.8279
val Loss: 0.3650 Acc: 0.8693
Epoch 2/24
----------
train Loss: 0.5586 Acc: 0.8320
val Loss: 0.2582 Acc: 0.9216
Epoch 3/24
----------
train Loss: 0.5923 Acc: 0.7500
val Loss: 0.2087 Acc: 0.9216
Epoch 4/24
----------
train Loss: 0.4039 Acc: 0.8238
val Loss: 0.2847 Acc: 0.9085
Epoch 5/24
----------
train Loss: 0.5311 Acc: 0.7951
val Loss: 0.2977 Acc: 0.8627
Epoch 6/24
----------
train Loss: 0.4319 Acc: 0.8238
val Loss: 0.3345 Acc: 0.8758
Epoch 7/24
----------
train Loss: 0.3792 Acc: 0.8402
val Loss: 0.2706 Acc: 0.8889
Epoch 8/24
----------
train Loss: 0.3567 Acc: 0.8320
val Loss: 0.2185 Acc: 0.8954
Epoch 9/24
----------
train Loss: 0.3523 Acc: 0.8484
val Loss: 0.2502 Acc: 0.9020
Epoch 10/24
----------
train Loss: 0.2898 Acc: 0.8975
val Loss: 0.2185 Acc: 0.9020
Epoch 11/24
----------
train Loss: 0.2386 Acc: 0.8934
val Loss: 0.2069 Acc: 0.9020
Epoch 12/24
----------
train Loss: 0.3540 Acc: 0.8443
val Loss: 0.2133 Acc: 0.9150
Epoch 13/24
----------
train Loss: 0.2405 Acc: 0.8975
val Loss: 0.2164 Acc: 0.9150
Epoch 14/24
----------
train Loss: 0.2776 Acc: 0.8852
val Loss: 0.2179 Acc: 0.9085
Epoch 15/24
----------
train Loss: 0.2287 Acc: 0.8975
val Loss: 0.2015 Acc: 0.9085
Epoch 16/24
----------
train Loss: 0.2239 Acc: 0.8852
val Loss: 0.2071 Acc: 0.9020
Epoch 17/24
----------
train Loss: 0.2254 Acc: 0.8975
val Loss: 0.2128 Acc: 0.9150
Epoch 18/24
----------
train Loss: 0.2929 Acc: 0.8770
val Loss: 0.2020 Acc: 0.8954
Epoch 19/24
----------
train Loss: 0.2991 Acc: 0.8730
val Loss: 0.2124 Acc: 0.9150
Epoch 20/24
----------
train Loss: 0.3283 Acc: 0.8689
val Loss: 0.2165 Acc: 0.9020
Epoch 21/24
----------
train Loss: 0.2522 Acc: 0.8934
val Loss: 0.2084 Acc: 0.9150
Epoch 22/24
----------
train Loss: 0.1911 Acc: 0.9303
val Loss: 0.2039 Acc: 0.8954
Epoch 23/24
----------
train Loss: 0.2316 Acc: 0.8934
val Loss: 0.1956 Acc: 0.9020
Epoch 24/24
----------
train Loss: 0.2631 Acc: 0.8811
val Loss: 0.1981 Acc: 0.9085
Training complete in 0m 34s
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: 0.5569 Acc: 0.7254
val Loss: 0.2273 Acc: 0.9477
Epoch 1/24
----------
train Loss: 0.4509 Acc: 0.7992
val Loss: 0.2381 Acc: 0.9216
Epoch 2/24
----------
train Loss: 0.3999 Acc: 0.8115
val Loss: 0.1870 Acc: 0.9542
Epoch 3/24
----------
train Loss: 0.5035 Acc: 0.7746
val Loss: 0.2274 Acc: 0.9281
Epoch 4/24
----------
train Loss: 0.4497 Acc: 0.8197
val Loss: 0.2063 Acc: 0.9542
Epoch 5/24
----------
train Loss: 0.5799 Acc: 0.7459
val Loss: 0.5657 Acc: 0.8105
Epoch 6/24
----------
train Loss: 0.4462 Acc: 0.7951
val Loss: 0.2067 Acc: 0.9412
Epoch 7/24
----------
train Loss: 0.3709 Acc: 0.8361
val Loss: 0.2137 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.3397 Acc: 0.8689
val Loss: 0.2068 Acc: 0.9542
Epoch 9/24
----------
train Loss: 0.3831 Acc: 0.8320
val Loss: 0.2099 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.2878 Acc: 0.8648
val Loss: 0.2396 Acc: 0.9216
Epoch 11/24
----------
train Loss: 0.3224 Acc: 0.8689
val Loss: 0.1991 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.3714 Acc: 0.8443
val Loss: 0.2052 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.2700 Acc: 0.8607
val Loss: 0.2086 Acc: 0.9477
Epoch 14/24
----------
train Loss: 0.3128 Acc: 0.8484
val Loss: 0.1943 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3727 Acc: 0.8361
val Loss: 0.1809 Acc: 0.9477
Epoch 16/24
----------
train Loss: 0.3552 Acc: 0.8443
val Loss: 0.1912 Acc: 0.9477
Epoch 17/24
----------
train Loss: 0.2999 Acc: 0.8975
val Loss: 0.1912 Acc: 0.9477
Epoch 18/24
----------
train Loss: 0.3448 Acc: 0.8566
val Loss: 0.1916 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.3175 Acc: 0.8648
val Loss: 0.2141 Acc: 0.9412
Epoch 20/24
----------
train Loss: 0.3485 Acc: 0.8484
val Loss: 0.1748 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3578 Acc: 0.8484
val Loss: 0.2234 Acc: 0.9346
Epoch 22/24
----------
train Loss: 0.3531 Acc: 0.8402
val Loss: 0.2355 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.4048 Acc: 0.8443
val Loss: 0.2726 Acc: 0.9150
Epoch 24/24
----------
train Loss: 0.3065 Acc: 0.8689
val Loss: 0.1958 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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