MobileNet-v3-Large-Quantized
Imagenet classifier and general purpose backbone.
MobileNet-v3-Large is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
Technical Details
Model checkpoint:Imagenet
Input resolution:224x224
Number of parameters:5.47M
Model size:5.79 MB
Applicable Scenarios
- Medical Imaging
- Anomaly Detection
- Inventory Management
Supported Form Factors
- Phone
- Tablet
- IoT
- XR
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.
- 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 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)
- 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
- Snapdragon® 8 Gen 1 Mobile
- Snapdragon® 8 Gen 2 Mobile
- Snapdragon® 8 Gen 3 Mobile
- Snapdragon® 888 Mobile