Image Classification Model

DeepLobe's Image Classification Model is designed to categorize images into different classes or labels based on their visual content. This model is widely used in various applications, such as object recognition, scene classification, and content-based image retrieval.

With DeepLobe's no-code AI platform, users can easily create custom Image Classification models without any coding knowledge. By leveraging the power of deep learning algorithms, the platform enables users to train models that can accurately classify images according to their assigned labels.

Whether you're working on a specific industry use case or need to classify images for a particular task, DeepLobe's Image Classification Model provides the flexibility and simplicity you need to achieve your goals.

Next, you can proceed to draft the steps and flow for creating a custom Image Classification model using DeepLobe.

Add Training Data

To build a custom Image Classification model in DeepLobe, you need to start by adding training data. The training data consists of images that will be used to teach the model how to classify different classes or labels. Here's how you can add training data:

Step 1: Create Classes and Upload Images

Create Class 1: Start by creating the first class for your image classification model. Give the class a name that represents the category or label you want to classify. For example, if you want to classify images of animals, you can create a class called "Cats."

Upload Images: Once you have created the class, you can begin uploading images that belong to that class. Select a minimum of 50 images that are relevant to the class you created. The images should be in jpg, png, or jpeg formats and should not exceed 1 MB in size. This ensures that the model has enough varied examples to learn from.

Repeat for Multiple Classes: If you have more than one class to classify, repeat the above steps for each class. Create a new class and upload a minimum of 50 images specific to that class. For example, you can create a class called "Dogs" and upload 50 dog images.

Step 2: Continue
Once you have uploaded the images for all the classes, click on the "Continue" button to proceed with the training process. DeepLobe will process the uploaded data and prepare it for training your custom Image Classification model.

By adding a sufficient number of images for each class, you provide the necessary training data for the model to learn and distinguish different visual patterns. This allows the model to accurately classify new images into the specified classes.

By adding training data with a diverse range of images representing each class, you provide the necessary information for the model to learn and generalize its understanding of different visual patterns. This forms the foundation for building an accurate and robust Image Classification model in DeepLobe.

Data Health Check

After uploading the training data for your custom Image Classification model in DeepLobe, it is important to perform a data health check. This step helps evaluate the quality and balance of your dataset and ensures that it is suitable for training a robust and accurate model. Here's how you can perform a data health check.

Data Split

DeepLobe automatically splits your uploaded training data into different subsets. Typically, the data is split into three categories: training data, validation data, and test data. The training data is used to train the model, the validation data is used to assess the model's performance during training, and the test data is used to evaluate the final model's accuracy.

Data Balance

It is crucial to check the balance of your dataset, which refers to the distribution of images across different classes. A well-balanced dataset has a similar number of images for each class, ensuring that the model receives sufficient examples to learn from for every class. DeepLobe provides tools to visualize the class distribution and identify any imbalances.

Class Imbalance

If you notice a significant imbalance in the number of images between classes, it is recommended to add more images to the underrepresented classes. This helps improve the model's ability to accurately classify images across all classes.

Data Quality

Check the quality of the images in your dataset. Ensure that they are clear, properly labeled, and representative of the class they belong to. Remove any low-quality or irrelevant images that might negatively impact the model's training process.

By performing a data health check, you ensure that your training data is of high quality, balanced across classes, and suitable for training a reliable Image Classification model. This step helps lay the foundation for a successful model training process.

Train Model

Once you have ensured the data health of your image classification model in DeepLobe, the next step is to train the model. Click on the "Train" button to initiate the training process. DeepLobe will start training the model using the uploaded training data and optimize it to learn the patterns and features of each class. You will receive an email notification once the model has completed training and is ready for use.

Evaluate and Test Model

To evaluate the accuracy of your trained image classification model, you can test it by uploading an image and clicking on the "Test" button. The model will make predictions for each class and display the results. If the predictions are not satisfactory, you have the option to retrain the model to improve its performance.

Retrain Model

DeepLobe provides the flexibility to retrain your image classification model for better outcomes. By adding more training data, you can increase the accuracy and effectiveness of your model. Simply upload your updated or additional data, and DeepLobe will handle the retraining process automatically. You can initiate the retraining process by clicking on the "Retrain" button on the results screen. Alternatively, you can navigate to the "My models" section, select the desired model, click on the three-vertical dots, and choose the "Retrain" option.

Model Integration

To integrate your image classification model into your applications or systems, you will need the API endpoint and API key. In the "My models" section, you can find the API endpoint for your image classification model. Click on the copy icon next to the API endpoint to save it. Additionally, generate the API key from the "API's" section and replace it in your code. The API key should be placed in the appropriate section of your code to authenticate your API requests.

For more detailed information and instructions on how to integrate the model using the API, refer to our API documentation.

By following these steps, you can successfully train, test, and retrain your custom image classification model using DeepLobe's no-code AI platform.