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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', 'ants', '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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96%|█████████▌| 42.8M/44.7M [00:00<00:00, 448MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 446MB/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: 0.6227 Acc: 0.6885
val Loss: 0.2800 Acc: 0.8758
Epoch 1/24
----------
train Loss: 0.4255 Acc: 0.7992
val Loss: 0.3663 Acc: 0.8824
Epoch 2/24
----------
train Loss: 0.4753 Acc: 0.8197
val Loss: 0.2656 Acc: 0.8954
Epoch 3/24
----------
train Loss: 0.3459 Acc: 0.8484
val Loss: 0.3395 Acc: 0.9085
Epoch 4/24
----------
train Loss: 0.3032 Acc: 0.8730
val Loss: 0.3348 Acc: 0.9085
Epoch 5/24
----------
train Loss: 0.3182 Acc: 0.8525
val Loss: 0.4186 Acc: 0.8758
Epoch 6/24
----------
train Loss: 0.5324 Acc: 0.7951
val Loss: 0.7371 Acc: 0.7843
Epoch 7/24
----------
train Loss: 0.3814 Acc: 0.8566
val Loss: 0.2707 Acc: 0.9216
Epoch 8/24
----------
train Loss: 0.3497 Acc: 0.8607
val Loss: 0.2883 Acc: 0.9085
Epoch 9/24
----------
train Loss: 0.3285 Acc: 0.8525
val Loss: 0.2659 Acc: 0.9085
Epoch 10/24
----------
train Loss: 0.2950 Acc: 0.8525
val Loss: 0.2657 Acc: 0.9281
Epoch 11/24
----------
train Loss: 0.3313 Acc: 0.8566
val Loss: 0.2652 Acc: 0.9281
Epoch 12/24
----------
train Loss: 0.3250 Acc: 0.8648
val Loss: 0.2494 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.2242 Acc: 0.9139
val Loss: 0.2486 Acc: 0.9281
Epoch 14/24
----------
train Loss: 0.3125 Acc: 0.8402
val Loss: 0.2514 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.2533 Acc: 0.8893
val Loss: 0.2473 Acc: 0.9150
Epoch 16/24
----------
train Loss: 0.2007 Acc: 0.9139
val Loss: 0.2459 Acc: 0.9216
Epoch 17/24
----------
train Loss: 0.2928 Acc: 0.8607
val Loss: 0.2437 Acc: 0.9216
Epoch 18/24
----------
train Loss: 0.3183 Acc: 0.8566
val Loss: 0.2573 Acc: 0.9216
Epoch 19/24
----------
train Loss: 0.3076 Acc: 0.8361
val Loss: 0.2432 Acc: 0.9281
Epoch 20/24
----------
train Loss: 0.2186 Acc: 0.9180
val Loss: 0.2504 Acc: 0.9281
Epoch 21/24
----------
train Loss: 0.2899 Acc: 0.8893
val Loss: 0.2416 Acc: 0.9216
Epoch 22/24
----------
train Loss: 0.2726 Acc: 0.8893
val Loss: 0.2323 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.2987 Acc: 0.8811
val Loss: 0.2461 Acc: 0.9216
Epoch 24/24
----------
train Loss: 0.2941 Acc: 0.9016
val Loss: 0.2522 Acc: 0.9346
Training complete in 0m 38s
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: 0.6769 Acc: 0.6148
val Loss: 0.2369 Acc: 0.9281
Epoch 1/24
----------
train Loss: 0.5926 Acc: 0.7131
val Loss: 0.5286 Acc: 0.7255
Epoch 2/24
----------
train Loss: 0.6062 Acc: 0.7500
val Loss: 0.2897 Acc: 0.8889
Epoch 3/24
----------
train Loss: 0.6048 Acc: 0.7459
val Loss: 0.3164 Acc: 0.8758
Epoch 4/24
----------
train Loss: 0.6358 Acc: 0.7623
val Loss: 0.2119 Acc: 0.9281
Epoch 5/24
----------
train Loss: 0.4138 Acc: 0.8074
val Loss: 0.1988 Acc: 0.9477
Epoch 6/24
----------
train Loss: 0.5236 Acc: 0.7664
val Loss: 0.1845 Acc: 0.9477
Epoch 7/24
----------
train Loss: 0.3492 Acc: 0.8770
val Loss: 0.2030 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.3464 Acc: 0.8607
val Loss: 0.2103 Acc: 0.9281
Epoch 9/24
----------
train Loss: 0.3161 Acc: 0.8484
val Loss: 0.1811 Acc: 0.9542
Epoch 10/24
----------
train Loss: 0.3881 Acc: 0.8484
val Loss: 0.1754 Acc: 0.9542
Epoch 11/24
----------
train Loss: 0.3413 Acc: 0.8689
val Loss: 0.1890 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.3594 Acc: 0.8484
val Loss: 0.2183 Acc: 0.9281
Epoch 13/24
----------
train Loss: 0.3227 Acc: 0.8443
val Loss: 0.1978 Acc: 0.9346
Epoch 14/24
----------
train Loss: 0.3868 Acc: 0.8320
val Loss: 0.2108 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.3417 Acc: 0.8238
val Loss: 0.1969 Acc: 0.9477
Epoch 16/24
----------
train Loss: 0.3114 Acc: 0.8566
val Loss: 0.1891 Acc: 0.9477
Epoch 17/24
----------
train Loss: 0.3380 Acc: 0.8689
val Loss: 0.2026 Acc: 0.9281
Epoch 18/24
----------
train Loss: 0.3839 Acc: 0.8320
val Loss: 0.1905 Acc: 0.9281
Epoch 19/24
----------
train Loss: 0.3114 Acc: 0.8689
val Loss: 0.1863 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.2429 Acc: 0.8852
val Loss: 0.1987 Acc: 0.9346
Epoch 21/24
----------
train Loss: 0.3457 Acc: 0.8484
val Loss: 0.1758 Acc: 0.9412
Epoch 22/24
----------
train Loss: 0.3044 Acc: 0.8811
val Loss: 0.1991 Acc: 0.9412
Epoch 23/24
----------
train Loss: 0.3521 Acc: 0.8361
val Loss: 0.1704 Acc: 0.9477
Epoch 24/24
----------
train Loss: 0.3082 Acc: 0.8730
val Loss: 0.1860 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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