FFNet-54S-Quantized
Semantic segmentation for automotive street scenes.
FFNet-54S-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:ffnet54S_dBBB_cityscapes_state_dict_quarts
Input resolution:2048x1024
Number of parameters:18.0M
Model size:17.5 MB
Number of output classes:19
Applicable Scenarios
- Automotive
- Autonomous Driving
- Camera
Supported Form Factors
- Phone
- Tablet
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 Devices
- Google Pixel 3
- Google Pixel 3a
- Google Pixel 3a XL
- Google Pixel 4
- Google Pixel 4a
- Google Pixel 5a 5G
- QCS6490 (Proxy)
- QCS8250 (Proxy)
- QCS8550 (Proxy)
- RB3 Gen 2 (Proxy)
- RB5 (Proxy)
- SA8255 (Proxy)
- SA8650 (Proxy)
- SA8775 (Proxy)
- Samsung Galaxy S21
- Samsung Galaxy S21 Ultra
- Samsung Galaxy S21+
- Samsung Galaxy S22 5G
- Samsung Galaxy S22 Ultra 5G
- Samsung Galaxy S22+ 5G
- Samsung Galaxy S23
- Samsung Galaxy S23 Ultra
- Samsung Galaxy S23+
- Samsung Galaxy S24
- Samsung Galaxy S24 Ultra
- Samsung Galaxy S24+
- Samsung Galaxy Tab S8
- Xiaomi 12
- Xiaomi 12 Pro
Supported Chipsets
- Qualcomm® QCS6490
- Qualcomm® QCS8250
- Qualcomm® QCS8550
- Qualcomm® SA8255P
- Qualcomm® SA8650P
- Qualcomm® SA8775P
- Snapdragon® 8 Gen 1 Mobile
- Snapdragon® 8 Gen 2 Mobile
- Snapdragon® 8 Gen 3 Mobile
- Snapdragon® 888 Mobile
- Snapdragon® X Elite