ResNeXt101Quantized
Imagenet classifier and general purpose backbone.
ResNeXt101 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:88.7M
Model size:87.3 MB
Applicable Scenarios
- Medical Imaging
- Anomaly Detection
- Inventory Management
Licenses
Source Model:BSD-3-CLAUSE
Deployable Model:AI Model Hub License
Tags
- 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.
- quantizedA “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.
Supported Compute Chipsets
- Snapdragon® X Elite