Unet-Segmentation
Real-time segmentation optimized for mobile and edge.
UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana-image-masking-challenge) and is used for vehicle segmentation.
Technical Details
Model checkpoint:unet_carvana_scale1.0_epoch2
Input resolution:224x224
Number of parameters:31.0M
Model size:118 MB
Number of output classes:2 (foreground / background)
Applicable Scenarios
- Autonomous Vehicles
- Medical Imaging
- Factory Quality Control
Tags
- backboneA “backbone” model is designed to extract task-agnostic representations from specific data modalities (e.g., images, text, speech). This representation can then be fine-tuned for specialized tasks.
- real-timeA “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.
Supported Compute Chipsets
- Snapdragon® X Elite