Developing a LeNet-5 architecture from scratch in PyTorch is as wild as creating a masterpiece from nothing. Just like a chef preparing a gourmet meal, start with resizing, converting, and normalizing to enhance flavor before diving into training. The result? A model with 6176 parameters ready to conquer any challenge, achieving an impressive 98.9% accuracy! Watch out for the link to the paper and my GitHub repository for all the code. Stay tuned for the next episode! ππ₯
Computer Vision in PyTorch: A Deep Dive ποΈ
Implementing LeNet-5 Architecture from Scratch
Computer vision enthusiasts often dive into the world of PyTorch to implement fascinating architectures like LeNet-5. This article will provide an in-depth guide on how to recreate the LeNet-5 model from scratch using PyTorch.
Key Takeaways:
- PyTorch offers a dynamic and flexible platform for creating computer vision models.
- LeNet-5 is a classic convolutional neural network architecture designed for image recognition tasks.
Understanding the LeNet-5 Architecture π§©
LeNet-5 is known for its groundbreaking design, with a unique architecture that has paved the way for modern CNNs. It comprises several layers of convolution and max-pooling, followed by fully connected layers. The input size of the images, kernel size, number of filters, and other parameters play a crucial role in defining this model’s structure.
Architecture Overview
| Layer | Input Shape | Output Shape |
|---|---|---|
| Input | 32x32x1 | – |
| Conv1 | 28x28x6 | – |
| Max Pooling 1 | 14x14x6 | – |
| Conv2 | 10x10x16 | – |
| Max Pooling 2 | 5x5x16 | – |
| Flatten | 400 (1D) | – |
| Fully Connected | 120 | – |
| Fully Connected | 84 | – |
| Output | 10 (num_classes) | – |
Setting up the Dataset and Preprocessing π¦
Before building the LeNet-5 model, we need to prepare the dataset by importing libraries, resizing the images, and normalizing them. For this guide, we’ll be using the MNIST dataset, consisting of 60,000 training images and 10,000 test images.
Dataset Overview
| Dataset Split | Number of Images |
|---|---|
| Training Set | 60,000 |
| Test Set | 10,000 |
Creating the LeNet-5 Model in PyTorch π₯οΈ
We’ll define the LeNet-5 model using PyTorch, leveraging the nn.Sequential container to stack the layers sequentially. This includes creating blocks for convolution, max-pooling, and fully connected layers, allowing us to formulate the network’s architecture precisely.
Training and Evaluating the LeNet-5 Model π
With the model architecture in place, we will define the training process and configure the loss function and optimizer. Training the model for several epochs will allow us to observe the decreasing loss and increasing accuracy, indicating the model’s improvement over time.
Training Performance
| Number of Epochs | Training Loss | Test Accuracy |
| —————– | ————— | ————– |
| 10 | Decreasing | 98.9% |
Conclusion π
In conclusion, we have successfully reconstructed the LeNet-5 architecture from scratch using PyTorch, trained the model, and achieved a remarkable accuracy of 98.9% on the test set. This demonstrates the efficacy and power of implementing classic computer vision models for modern applications.
FAQ
- What is the significance of the LeNet-5 architecture?
- LeNet-5’s design paved the way for modern convolutional neural networks and image recognition techniques.
With the detailed guide provided in this article, enthusiasts and practitioners can embrace the journey of diving deep into the world of computer vision using PyTorch, especially when recreating classic architectures like LeNet-5. Happy coding! π
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