DeepLabV3-Plus-MobileNet
Deep Convolutional Neural Network model for semantic segmentation.
DeepLabV3 is designed for semantic segmentation at multiple scales, trained on the various datasets. It uses MobileNet as a backbone.
12.9ms
Inference Time
77.3inferences / s
Throughput
0 ‑ 11MB
Memory Usage
101NPU
Layers
TorchScript to Qualcomm® AI Runtime
43.9ms
Inference Time
22.8inferences / s
Throughput
2 ‑ 91MB
Memory Usage
85NPU
Layers
16.5ms
Inference Time
60.7inferences / s
Throughput
0 ‑ 43MB
Memory Usage
136NPU
Layers
Technical Details
Model checkpoint:VOC2012
Input resolution:513x513
Number of output classes:21
Number of parameters:5.80M
Model size (float):22.2 MB
Model size (w8a16):6.67 MB
Applicable Scenarios
- Anomaly Detection
- Inventory Management
Licenses
Source Model:MIT
Deployable Model:AI-HUB-MODELS-LICENSE
Supported IoT Devices
- QCS6490 (Proxy)
- QCS8250 (Proxy)
- QCS8275 (Proxy)
- QCS8550 (Proxy)
- QCS9075 (Proxy)
- RB3 Gen 2 (Proxy)
- RB5 (Proxy)
Supported IoT Chipsets
- Qualcomm® QCS6490 (Proxy)
- Qualcomm® QCS8250 (Proxy)
- Qualcomm® QCS8275 (Proxy)
- Qualcomm® QCS8550 (Proxy)
- Qualcomm® QCS9075 (Proxy)
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