Developing computer vision applications based on Nexa AI typically follows the standardized process below:
- Model selection phase::
- Identify specific task types (e.g., image classification, target detection, semantic segmentation, etc.) <li]Filter quantized pre-trained models in the model library, prioritizing versions labeled 'high precision' or 'lightweight'
- environmental preparation::
- Ensure that basic computer vision libraries such as OpenCV are installed
- Configure the Python development environment (version 3.8+ recommended)
- Core Development Steps::
- Load the model via the SDK:
from nexa import VisionModel
model = VisionModel.load('image_classification_v3') - Implementation of image preprocessing logic: including normalization, resizing and other operations
- Perform reasoning tasks:
results = model.predict(image_path) - Processing the output: parsing structured data such as confidence levels, bounding boxes, etc.
- Load the model via the SDK:
- performance optimization::
- Enable GPU acceleration (if applicable)
- Increase Throughput with Batch Processing
- Model fine-tuning for specific scenarios (optional)
Typical application scenarios include: defect detection in industrial quality inspection, human flow analysis in retail scenarios, and assisted diagnosis in medical imaging. The platform provides complete visual processing pipeline sample code, developers can quickly start projects based on these templates.
This answer comes from the articleNexa: a small multimodal AI solution for local operationThe
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