A Computer Vision Solution for Everyone
Dataguess CV STUDIO
Quickly and easily develop Dataguess' Computer Vision products on this platform without writing code. Create as many datasets as you want and train and test dozens of different models.
Take images from cameras with a single click or upload existing images to the system to train AI models.
Quickly label the model you created with labeling tools and create a dataset. Deploy the trained models you have developed on any Edge device or in the cloud.
Creating a Dataset
The creation and labeling of datasets is crucial for Computer Vision applications in order to ensure that AI algorithms yield dependable and precise results. The following procedures are followed in order to create a dataset:
Data Collection: Gathering relevant data for the problem domain is the first step. With Dataguess CV Studio, gathering this data is simple and includes photos or videos captured by cameras or other sensors.
Data Pre-Processing: The collected data is pre-processed to remove errors such as unwanted noise, distortions, or duplicate samples. *
Data Labeling: The labels applied to the images you have gathered are based on the intended use of the application. To develop an object or human data for a project, all that is needed is to square it or mark it with multiple selection points. You can simply complete the labeling process using third-party data labeling applications in addition to Dataguess CV Studio.
Data Partitioning: Data is partitioned into training and test sets. While the training set is used to train the model, the test set is used to evaluate the model's performance. *
Data Enrichment: Data is enriched to make AI algorithms perform better. For example, data is enhanced by techniques such as contrast enhancement or color editing. *
Data Minimization: Large datasets require excessive memory usage and can therefore slow down the model training process. Therefore, the size of the dataset should be reduced by using sampling techniques. *
In order for artificial intelligence models to produce accurate and reliable results, dataset creation and labeling steps should be carried out carefully, and quality control should be performed.
Items marked with an asterisk (*) are completed automatically by Dataguess CV Studio.
Creating a Computer Vision Project and Building an Artificial Intelligence Model
You can create any project without writing code using the five basic products included in Dataguess CV Studio.
Dataguess Inspector: Quality Control and Absence Detection
Dataguess Counter: Product Classification and Counting
Dataguess Observer: People Counting and Area Density Mapping
Dataguess Guard: OHS and Safety
Dataguess Tracer: Marker and Product Trolley Tracking
In order to prepare the artificial intelligence models that you develop as part of your project, you utilize the Model Creation Wizard to select the model and establish its parameters prior to training.
Model Selection: You should select a suitable CV model for the problem. When you do not make a selection, the model with the best performance is determined automatically. Dataguess CV Studio includes neural network-based models, particularly deep learning models. The size, structure, requirements, and application purpose of the dataset all play a role in the model choice.
Model Parameters Selection: The Model Creation Wizard allows you to select the parameters needed to train the dataset. The parameters that yield optimal performance are automatically determined when you don't make a selection. The model's accuracy and performance can be enhanced by adjusting parameters like learning rate, epoch count, and hyperparameters.
Artificial Intelligence Model Training
Dataguess CV Studio uses the model type and training parameters you enter in the Model Creation Wizard to carry out the training process.
A comparison is made between the performance of the model on the training dataset and the test dataset. Among other performance metrics, the accuracy, sensitivity, specificity, and F1 score of the model are assessed. Upon completion of training, these metrics are presented for your review in the model details.
At this stage, protecting the model from overfitting issues and accurately evaluating its performance on the test set are critical for efficiency. The prediction performance of the model will improve as more data collection, model type, and training parameters are investigated.
Main Features
Upload, label, and categorize any photo or video using the labeling interface. Also, update datasets at any time.
Create Your Datasets and Your Computer Vision Library
NO-CODE
NO-CODE
You Don't Need to Know Coding
Programming experience is not required to use Dataguess CV Studio, a solution that has an intuitive user interface and does not require a lot of software knowledge. This sophisticated application is made to be equally useful for both software engineers and non-developers.
You can easily train your AI model without the need for external assistance, and when you make an update, you can retrain your model and keep using it.
Train as Many Models as You Need or Update Existing Ones
Integrating Dataguess CV Studio into your existing IT systems is easy due to its compatibility with standard communication protocols.
Integrate into your IT Systems
Model Deployment
Easily deploy the model you trained in any of the Dataguess CV Studio products to a computer on the Edge or in the cloud.
