This is a model that detects objects in an image and returns the image with labeled bounding boxes around the detected objects.
It is based on the YOLOv4 model. YOLOv4 uses an extended Convolutional Neural Network architecture. For more information about the underlying Neural Network and further resources, you can read our blog post: https://ubiops.com/how-to-deploy-yolov4-on-ubiops/. It is implemented on UbiOps with ONNX for speedup.
Detects objects in an image and returns the image with labeled bounding boxes around the detected objects. Expects a jpg/jpeg or .png file as input. Upload your image and click ‘‘run code”.
To deploy this example model to your own UbiOps project you can log in to the WebApp and create a new deployment in the deployment tab. We have created a full blog post about it.
In that tutorial, we will take a look at one of these image recognition models called YOLOv4 (You only look once) and install it locally. Take a look at the paper and the website from the author. ubiops.com/how-to-deploy-yolov4-on-ubiops/ubiops.com/docs/ubiops_tutorials/ready-deployments/multiplication/multiplication/
For this tutorial, we will use a pre-trained version of the YOLOv4 model from the internet. Pre-trained models are great because you can use them right away or as a starting point for your own specialized models. This saves a lot of time.
This model is intended for demonstration and testing purposes only. UbiOps is not liable for any damages arising from the use or inability to use any of the models and applications listed on the UbiOps Community Model pages. Even though UbiOps and our partners carefully created and optimized these models, it is always advised to benchmark and check the respective functionality before applying it in any production setting.
Last modified: 30-11-2021
Detects objects in an image and returns the image with labeled bounding boxes around the detected objects.