Object Detection Model

DeepLobe’s Object Detection Model is a powerful computer vision tool that enables the detection and localization of objects within images or videos. It automates the process of identifying and outlining specific objects of interest, providing valuable insights and enabling a wide range of applications.

Object detection plays a vital role in various fields, including autonomous driving, surveillance systems, retail analytics, and more. DeepLobe's Object Detection Model goes beyond simple classification by not only identifying objects but also providing precise bounding box coordinates that delineate the object's location within the image.

DeepLobe's Object Detection Model leverages advanced machine learning algorithms, including deep neural networks, to analyze visual data and identify objects based on their features and patterns. The model has been trained on extensive datasets, enabling it to detect a wide variety of objects accurately.

By utilizing DeepLobe's Object Detection Model, users can automate object detection tasks, streamline workflows, and gain valuable insights from visual data. Whether it's counting objects, tracking their movement, or analyzing their attributes, the Object Detection Model empowers users to unlock the potential of computer vision in their applications.

Next, we will guide you through the steps and flow of creating a custom Object Detection Model using DeepLobe's intuitive and user-friendly platform.

Create Custom Object Detection Model

1. Click on create Object Detection Model:
2. Begin by creating a new project for your Object Detection model in DeepLobe.
3. Provide a suitable name and description to identify your project.

Add Training Data

You can add train data in multiple ways. 

Upload Images

You can upload images to DeepLobe to train your Object Detection model. To upload images: Click on the "Browse files" button on the project creation screen. Locate and select the images from your local storage.Ensure that your images are in JPG, PNG, or JPEG format and meet the following requirements:

• Minimum of 50 files
• Each file size should not exceed 1 MB

Import from Data Sources

You can also import images from data sources like OneDrive and Google Drive.

Upload Videos

If you have video data for your Object Detection model, you can upload .mp4 format videos to DeepLobe. Follow these steps:

• Click on the "Browse files" button on the project creation screen.
• Locate and select the video files from your local storage.
• When uploading videos, you need to choose the number of frames per second (fps) for sampling.
• Higher fps will result in a larger output size with more images. Adjust the frame rate using the slider and click on "Choose Frame Rate" to confirm your selection.
• DeepLobe will convert the video into a sequence of images based on the chosen frame rate. Please note that these frames/images will not be annotated, so you will need to manually annotate them or hire a professional annotator.

Add Data using JSON files

Additionally, you have the option to upload annotated data in .json format. If you have already annotated your data externally, you can upload the annotations to DeepLobe. This allows you to utilize pre-existing annotations and speed up the training process. To add data using JSON files:

• Click on the "Upload JSON" button on the project creation screen.
• Select the JSON file containing the annotations.
• DeepLobe will use this annotated data to train your Object Detection model.
• Once you have uploaded your data, DeepLobe provides annotation tools and functionalities to annotate your images manually or with the help of a professional annotator.

It's important to have accurate and comprehensive annotations for training your Object Detection model effectively. Well-annotated data helps the model learn and identify objects accurately in images or videos.

Annotate Data

To annotate your dataset for the custom Object Detection Model, DeepLobe provides a user-friendly annotation tool. Here are the next steps for annotation:

Upload and Prepare Your Dataset

To annotate your dataset for the custom Object Detection Model, DeepLobe provides a user-friendly anno
Make sure you have uploaded a minimum of 50 files for your dataset. Once uploaded, click on "Continue" to proceed to the labeling tool.

Use the Annotation Tool

DeepLobe's in-built annotation tool allows you to manually label objects in your images. Follow these steps:

• An image from your dataset will be displayed in the labeling tool.
• To label an object or region of interest, draw a bounding box around it. Click and drag the cursor to form a tight box around the object. The box should encapsulate the entire object or region.
• Once the bounding box is formed, select it to assign a label or class. Click on the "Add label" button and enter the appropriate label, such as "person" or "car." You can add multiple labels for different objects in the image.
• Repeat this process for each object or region of interest in the image. Continue to the next image when you have finished annotating the current one.

Review and Edit Annotations

After annotating an image, you can review and edit the annotations if needed. You can adjust the position and size of the bounding boxes, modify labels, or delete incorrect annotations. Ensure that the annotations accurately represent the objects or regions of interest in the image.

Once you have completed annotating all the images in your dataset, the tool automatically saves your annotations and clicks on the train. DeepLobe provides options to save annotations as JSON or other compatible formats for further use.

If you prefer to have a professional annotator assist you, you can click on "Contact Us" and select "Hire Annotation Expert" to request this service.

The bounding box approach used in DeepLobe's annotation tool involves drawing a box around the object of interest. It is defined by a pair of coordinates, width, and height. Each bounding box needs to be accompanied by a corresponding label or class, describing the object it encloses.

Accurate and comprehensive annotations are crucial for training your Object Detection model effectively. They help the model learn to recognize and classify objects with accuracy.

Data Health Check

In the custom Object Detection model flow, DeepLobe provides a Data Health Check stage to evaluate the quality and balance of your training and validation data sets. This stage allows you to assess the health of your data and take necessary actions to improve the precision and performance of your models.

DeepLobe offers powerful analysis tools, such as Class Balance, to assist you in identifying potential gaps or imbalances within your data. These tools highlight classes with a minority of data, making it easier for you to identify areas where additional data is needed to improve the model's performance.

To improve the health of your data and enhance the accuracy of your model, consider the following steps:

Add More Data

If the analysis reveals that certain classes have a limited number of samples, it is advisable to add more data for those classes. Collecting additional samples will provide a more diverse and representative training set, enabling the model to learn effectively.

Evenly Distribute Training Data

Ensure that your training data is evenly distributed across different classes. Imbalanced training data, where certain classes have significantly more samples than others, can lead to biased models. By evenly distributing the training data, you create a balanced learning environment that promotes fair and accurate predictions for all classes.

By following these steps, you can improve the quality and health of your data, leading to better model performance and more reliable object detection results.

DeepLobe's Data Health Check stage empowers you to evaluate and optimize your training and validation data sets, ultimately enhancing the effectiveness and accuracy of your custom object detection models.

Train Model

Once the data health of your custom object detection model is verified and deemed satisfactory, you can proceed to train your model by clicking on the "Train" button. The training process involves using the labeled data to teach the model how to identify and classify objects in images accurately.

Upon initiating the training process, DeepLobe will commence training your model using the provided data. The training time may vary depending on the size of the dataset and the complexity of the objects being detected. Once the training is completed, you will receive an email notification indicating that your model has been trained and is ready for use.

During the training phase, DeepLobe's advanced algorithms analyze the data and optimize the model's parameters to improve its accuracy and performance. The model learns to recognize patterns, features, and characteristics of the objects within the training data, enabling it to make accurate predictions on new, unseen images.

It's important to note that the training process may require multiple iterations and adjustments to fine-tune the model's performance. DeepLobe's training algorithm optimizes the model based on feedback and evaluation metrics, such as precision, recall, and F1 score, to ensure optimal results.

Once you receive the notification that your model has been trained and is ready, you can proceed to the next stage, which involves evaluating and testing the model's performance using the validation and test datasets.

Evaluate and Test Model

Once your object detection model has been trained, it is important to evaluate its performance and test its accuracy. DeepLobe provides tools to help you with this process.

To test the model, you can upload any image of your choice and click on the "Test" button. The model will analyze the image and accurately detect the objects within it. It will then provide the correct labels for each detected object. This allows you to assess the model's performance and verify if it meets your expectations in terms of accuracy and object detection.

Retrain Model

If you find that the model's accuracy is not satisfactory, DeepLobe offers a retraining capability to help improve the model's performance. By adding more training data to the model, you can enhance its accuracy and increase its ability to correctly detect and classify objects. Simply upload your updated or additional data, and DeepLobe will take care of the retraining process. This makes it easy to iterate and improve your model with just a few clicks.

To initiate the retraining process, you can click on the "Retrain" button present on the results screen after testing the model. Additionally, you can navigate to the "My Models" section in the left panel of the DeepLobe platform. From there, you can select the specific model you want to retrain and click on the three-vertical dots corresponding to that model. Then, choose the "Retrain" option to start the retraining process.

By incorporating more data and retraining the model, you can achieve greater accuracy and performance in your object detection tasks. DeepLobe simplifies the retraining process, allowing you to optimize your model's performance with ease.

For more detailed guidance on evaluating model metrics, testing your model, and retraining, you can refer to the DeepLobe documentation or reach out to our support team for further assistance. We are here to help you achieve the best results with your custom object detection models.

Integrate Model

To integrate your object detection model into your applications or systems, you will need the model API endpoint and the corresponding API key. Follow these steps to obtain the API endpoint for your object detection model:

• Navigate to the "My Models" section in the DeepLobe platform.
• Locate your object detection model from the list of models.
• Look for the API endpoint associated with your model. It will be displayed in the platform, usually in the format of a URL or an endpoint address.
• Click on the copy icon located next to the API endpoint to copy it to your clipboard.
• Once you have copied the API endpoint, you can use it to make API requests and integrate your object detection model into your applications or systems.

Note: The API endpoint is specific to your object detection model and allows you to communicate with the DeepLobe platform to utilize the model's capabilities.

For more information on how to use the API endpoint and integrate the model into your applications, you can refer to the DeepLobe API documentation or consult the relevant documentation for your programming language or framework.

API Key: To generate an API key for your DeepLobe account, follow these steps:

• Go to the "APIs" section in the DeepLobe platform.
• Look for the API key associated with your account.
• Click on the copy icon located next to the API key to copy it to your clipboard.
• Once you have obtained the API key, you can use it to authenticate and authorize your API requests to the DeepLobe platform.

For more information, go through our API documentation.