Tutorial: Image Classification Using DeepVision

Welcome to this tutorial on using DeepVision for image classification. In this guide, we'll walk through the process of using our machine learning library to classify images into predefined categories.

Prerequisites

Before starting, make sure you have:

• Python 3.6 or higher installed.
• DeepVision library installed (pip install deepvision).
• Basic understanding of Python and machine learning concepts.

Step 1: Setting Up Your Environment

First, let's set up our working environment:

1. Create a new Python virtual environment (recommended):

   python -m venv deepvision_env
   source deepvision_env/bin/activate

2. Install DeepVision:

   pip install deepvision

Step 2: Prepare Your Dataset

For this tutorial, we'll use a simple dataset consisting of images of cats and dogs. Ensure your dataset is structured as follows:

dataset/
    cats/
        cat1.jpg
        cat2.jpg
        ...
    dogs/
        dog1.jpg
        dog2.jpg
        ...

Step 3: Importing Libraries

In your Python script or Jupyter notebook, import the necessary libraries:

import deepvision as dv
import os

Step 4: Loading and Preprocessing the Data

DeepVision makes it easy to load and preprocess your data:

# Path to your dataset
data_path = 'path/to/dataset'
 
# Load dataset
dataset = dv.datasets.load_from_directory(data_path, target_size=(200, 200), batch_size=32)

Step 5: Building the Model

Let's build a simple convolutional neural network (CNN) for our classification task:

model = dv.models.Sequential()
model.add(dv.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(200, 200, 3)))
model.add(dv.layers.MaxPooling2D(2, 2))
model.add(dv.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(dv.layers.MaxPooling2D(2, 2))
model.add(dv.layers.Flatten())
model.add(dv.layers.Dense(64, activation='relu'))
model.add(dv.layers.Dense(2, activation='softmax'))  # 2 categories: cats and dogs

Step 6: Compile the Model

Compile the model with an appropriate optimizer, loss function, and metrics:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Step 7: Training the Model

Train the model using the dataset:

model.fit(dataset, epochs=10)

Step 8: Evaluating the Model

Finally, evaluate the performance of your model:

loss, accuracy = model.evaluate(dataset)
print(f"Model accuracy: {accuracy*100}%")

Conclusion

Congratulations! You've successfully built and trained an image classification model using DeepVision. This is a basic example, and DeepVision offers much more flexibility and options for building sophisticated machine learning models.

For more advanced tutorials and documentation, visit [DeepVision's official documentation].