Deep Learning Project: Plant Disease Detection | PyTorch | Creating Custom Dataset

Описание: End to End Deep Learning Project | Convolutional Neural Networks (CNNs) | Image Classification using PyTorch | Plant Disease detection.
In this Deep Learning end-to-end project, we begin building a Plant Disease Identification model using PyTorch.

📌 What you will learn:
🔹 Load image data using the `ImageFolder` class
🔹 Investigate the dataset object and sample class labels
🔹 Filter and sample specific plant disease categories
🔹 Create a custom PyTorch Dataset class for more control
🔹 Override __len__() and __getitem__() methods
🔹 Instantiate a custom dataset object with filtered data
🔹 Visualize transformed data as images using matplotlib

Whether you're a beginner or looking to deepen your PyTorch skills, this tutorial will guide you step-by-step in preparing your image dataset for plant disease classification.
✅ What’s next? We will train CNN models on this dataset to detect diseases like early blight, bacterial spot, and more!
✅ Tools: Python, PyTorch, torchvision

Watch our other playlists here :
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🎯 Who is this for?
Perfect for beginners and intermediates in deep learning who want a structured and practical approach to building AI models. Whether you're prepping for a data science interview or looking to build your own projects, mastering deep learning algorithms will set a strong foundation for more advanced machine learning techniques.

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Time breaks:
00:00 Project Introduction.
02:19 Plant Disease dataset.
04:49 Using ImageFolder for loading data.
06:42 Exploring dataset object.
08:25 Filtering the data.
12:40 Updating class labels for filtered samples.
17:00 Creating custom dataset class.
19:47 Method overriding : _len__, __getitem_
22:13 Creating custom dataset object.
25:21 Display transformed data as an image.


If you're learning Machine Learning, Deep Learning, or AI, this video will provide you with a solid foundation to implement your own models. Don't forget to hit like, comment, and subscribe to keep learning with me!
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