FFNet-78S-Quantized
Semantic segmentation for automotive street scenes.
FFNet-78S-Quantized is a "fuss-free network" that segments street scene images with per-pixel classes like road, sidewalk, and pedestrian. Trained on the Cityscapes dataset.
Technical Details
Model checkpoint:ffnet78S_dBBB_cityscapes_state_dict_quarts
Input resolution:2048x1024
Number of parameters:27.5M
Model size:26.7 MB
Number of output classes:19
Applicable Scenarios
- Automotive
- Autonomous Driving
- Camera
Licenses
Source Model:BSD-3-CLAUSE
Deployable Model:AI Model Hub License
Tags
- quantizedA “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.
- 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