Gain insights into image classification with PyTorch. Learn about data preprocessing, model training, fine-tuning, and deploying models using ONNX for real-world applications.

Introduction to PyTorch image classification-01.png

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PyTorch is a machine learning framework used in a wide array of popular applications, including Tesla’s Autopilot and Pyro, Uber’s probabilistic modeling engine.

This course is an introduction to image classification using PyTorch’s computer vision models for training and tuning your own model. You’ll start with the fundamental concepts of applying machine learning and its applications to image classification before exploring the process of training your AI model. You’ll prepare data for intake by the computer vision model with image pre-processing, set up pipelines for training your model, and fine-tune the variables to improve predictive performance. You’ll finish by deploying the image classification model by converting to ONNX format and serving it via REST API.

By the end of this course, you’ll be able to build and deploy your own image classification models from scratch.

Getting Started with Image Classification with PyTorch

Which loss function is unavailable in the PyTorch Image Model library?

Which loss function is unavailable in the PyTorch Image Model library?

Which argument represents the `Cutmix` lambda value for the `SoftTargetCrossEntropy` module?

Which argument represents the <code>Cutmix</code> lambda value for the <code>SoftTargetCrossEntropy</code> module?

The Pytorch Image Model provides two asymmetric loss modules: namely `AsymmetricLossSingleLabel` and `AsymmetricLossMultiLabel`.

Asymmetric loss is easy to implement with an increase in complexity and training time.

`AsymmetricLossMultiLabel` is an ideal choice for multiclass classification tasks.

Asymmetric loss treats positive and negative samples the same.

Which statement about the asymmetric loss in the PyTorch Image Model is correct?

Which statement about the asymmetric loss in the PyTorch Image Model is correct?

```
asl = AsymmetricLossSingleLabel(gamma_pos=2, gamma_neg=4, eps=0.5)
print(asl(output, labels))
```

```
asl = AsymmetricLossSingleLabel(gamma_pos=1, gamma_neg=5, eps=0.1)
print(asl(output, labels))
```

```
asl = AsymmetricLossSingleLabel()
print(asl(output, labels))
```

```
asl = AsymmetricLossSingleLabel(gamma_pos=0, gamma_neg=0, eps=0.0)
print(asl(output, labels))
```

```
import timm
import torch
import torch.nn.functional as F
from timm.loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
from PIL import Image

output = F.one_hot(torch.tensor([0,9,0])).float()
labels=torch.tensor([0,0,0])

...
```

Which will print the same output as the code snippet below?

```
print(F.cross_entropy(output, labels))
```

<pre><code>import timm
import torch
import torch.nn.functional as F
from timm.loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
from PIL import Image

output = F.one_hot(torch.tensor([0,9,0])).float()
labels=torch.tensor([0,0,0])

...
</code></pre>
Which will print the same output as the code snippet below?
<pre><code>print(F.cross_entropy(output, labels))
</code></pre>

Which argument represents the smoothing value for the `JsdCrossEntropy` module?

Which argument represents the smoothing value for the <code>JsdCrossEntropy</code> module?

Loss Functions

Loss Functions

Test your knowledge of the loss functions in the Pytorch Image Model.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Anomaly Detection in Medical Images: Python, TF, and PyTorch

Deployment

Appendix

Conclusion

Quiz (Loss)