Capture with a Phone
The examples below will show recording of a five second video and image captures of coffee cups using a phone for training a Vision model that detects coffee cups. However, you can choose any type of objects in your dataset.
Data Usage
It is recommended to use a phone connected to a Wifi network. A device connected to mobile data might be subject to intense usage when uploading files; video files or image files can be large in size. In the examples below, the video file used was ~15MB and the image files were ~2MB each.
Record Video
Using a smartphone, you can record a video with the camera application as shown below. Typically, the video recording can be started by pressing the red circular button. The video can be stopped by pressing the same button again.
Capture Images
Furthermore, you can also capture individual images as shown below. You can take image snapshots from the camera by pressing the white circular button.
Leveraging Videos
It is recommended to use videos rather than individual images. This is because the Automatic Ground Truth Generation (AGTG) feature leverages SAM-2 with tracking information which only needs a single annotation to annotate all frames. However, individual images requires more effort by annotating each image separately.
Limited Datasets
Throughout the demos, the dataset is kept small. However, training on limited datasets will result in poor model performances when the model is deployed under conditions that differs from the dataset samples. It is suggested to increase the amount of training data under various conditions and backgrounds to train a more robust model.
Create Dataset
If you have a video recording or sample images for your dataset, you can create a dataset container in EdgeFirst Studio to contain your video frames or images and annotations.
Navigate to a web browser and login to EdgeFirst Studio. Once logged in to EdgeFirst Studio, navigate to your project. In this case the project name is "Object Detection". Click on the "Datasets" button that is indicated in red below.
This will bring you to the "Datasets" page of the selected project. Create a new dataset container by clicking the "New Dataset" button that is indicated in red.
Add the dataset and annotation container name, labels, and dataset description as indicated by the fields below. It is up to you to specify the information in the fields and you do not have to strictly follow the example shown below. Click the "Create" button once the fields have been filled.
Your created dataset will look as follows.
Upload Video
Video files can be uploaded into any dataset container in EdgeFirst Studio. Choose the dataset container to upload the video file. In this case, the dataset is called "Coffee Cup". Click on the dataset context menu (three dots) and select import.
This will bring you to the "Import Dataset" page.
Click on the drop-down that says "Import Type" and then specify "Videos" and then click "Done" as shown below.
Now that the import type is specified to a "Videos", click on "select files" as indicated.
On an android device, this will bring up the option to specify the location of the files.
In my current setup, I have selected "My Files" from the options above and then "Videos" which will allow me to pinpoint the location of the video I have recorded.
Once the video file has been selected, set the desired FPS (frames per second), and then go ahead and click the "Start Import" button to start importing the video file.
This will start the import process and once it is completed, you should see the number of images in the dataset increased. If you do not see any changes, refresh the browser.
Upload Images
HEIC is not fully supported
Apple devices capture HEIC image formats by default. This format has not been fully supported yet in EdgeFirst Studio. Please make sure to use JPEGs, JPGs, or PNGs for uploading images to EdgeFirst Studio.
Image files can be uploaded into any dataset container in EdgeFirst Studio. Choose the dataset container to upload image files. In this case, the dataset is called "Coffee Cup". Click on the dataset context menu (three dots) and select import.
This will bring you to the "Import Dataset" page.
Click on "select files". This will bring up the option to specify the location of the files.
In my current setup, I have selected "Photos & Videos" from the options above and then I have multi-selected the images I want to import by press and hold on a single image to enable multi-select. To import, I pressed "Select".
Once the image files have been selected, the progress for the image import will be shown.
Once it completes, you should see the number of images in the dataset increase by the amount of selected images. If you do not see any changes, refresh your browser.
Next view the gallery of the dataset to confirm all the captured data has been uploaded. You should see the imported video file and images in the gallery. Note that videos appear as sequences with a play button overlay on the preview thumbnail.
Once all the captured data has been uploaded to the dataset container, you will now assign groups to the data to split the data into training and validation sets. Follow the tutorial for creating groups with an 80% partition to training and 20% partition to validation. The final outcome for the groups should look as follows.
Now that you have imported captured images or videos into EdgeFirst Studio and have split the captured data into training and validation partitions, you can now start annotating your data as shown in the next section below.