A simple, beginener friendly pizza, steak and sushi classifying AI!
What began as a straightforward endeavor formed out of boredom developed into a fascinating voyage of discovery, education, and creativity. The pizza, sushi, and steak classifier, which is designed to anticipate them, is an example of how AI may be used to perform image recognition and classification tasks. This project serves as a beginner-friendly introduction to the fascinating area of artificial intelligence and its use in computer vision by reducing the dataset and offering a simple methodology. I hope that my endeavor encourages people to start their own AI projects, exploring novel ideas and turning boredom into a creative force.
- Open the Repository in Google Colab:
- Once you are on the repository page, click on the "Open in Colab" button. This will open the entire project in a Google Colab notebook.
- Set Up Google Colab:
- If you are new to Google Colab, you might need to sign in with your Google account or create one if you don't have it already. This step is essential to save your progress and access the Colab environment.
- Familiarize Yourself with the Notebook:
- Take a moment to review the project notebook.
- Run the Code Cells:
- The notebook is divided into code cells. To execute a cell, click on it, and either press "Shift + Enter" or click on the "play" button on the left of the cell. This will run the code in that specific cell.
- Upload an Image:
- Following the notebook's instructions, locate the "Predicting on new/custom images" section where you can upload your own image to test the food classifier. You can do this by running the corresponding code cell.
- Get Predictions and Probabilities:
- After uploading the image, the classifier will analyze the uploaded image and predict whether it contains pizza, sushi, or steak. The probability of the predicted class will also be displayed, indicating the model's confidence in its prediction.
- Interpret the Results:
- Examine the output to see the model's predictions and the probabilities. This will give you an insight into the model's accuracy and its level of certainty for each class.
- Experiment and Enjoy:
- Feel free to test the model with different images of pizza, sushi, and steak. Observe how the classifier performs with various inputs and discover the potential of the AI-based food classifier.
- Share and Learn:
- As you explore the project, don't hesitate to share your experience with others. Collaborate, ask questions, and learn from the community. Engaging with others will enrich your understanding and passion for AI and computer vision.
None left!! Yayyy!?!