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
- quantized
- real-time
Supported IoT Devices
- QCS6490 (Proxy)
- QCS8250 (Proxy)
- QCS8550 (Proxy)
- RB3 Gen 2 (Proxy)
- RB5 (Proxy)
Supported IoT Chipsets
- Qualcomm® QCS6490 (Proxy)
- Qualcomm® QCS8250 (Proxy)
- Qualcomm® QCS8550 (Proxy)
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