Model Download (Beta)
An AI model training pipeline is a series of interconnected steps that automate the process of training, validating, and fine-tuning a machine learning model. DataVerse simplifies the creation of an AI model training pipeline, allowing users to focus on obtaining the best results for their specific use cases. This guide will walk you through the process of creating an AI model training pipeline using the DataVerse platform.
Hint
You can perform model download on the Model.
Quick Start
Download model.
Click on the video below for a quick overview:
Download model by dataverse-sdk
DataVerse-sdk install:
pip install dataverse-sdk
detail link: https://pypi.org/project/dataverse-sdk/
Download Steps
Model Content in DataVerse
BOX2D models:
Model Code | Input Image Size | Description |
1 | (416, 416) | Tiny and Fast: Ideal for quick prototyping and low resource scenarios, this model provides reasonable accuracy with a smaller input size of 416x416 pixels. |
2 | (640, 640) | Balanced Small: A perfect blend of efficiency and performance, this model provides improved accuracy while maintaining fast performance with a input size of 640x640 pixels. |
3 | (640, 640) | Standard: The go-to choice for most users, this model balances speed and accuracy with a moderate input size of 640x640 pixels. |
4 | (640, 640) | Advanced: With extended features for better accuracy, this model may take a bit more time but delivers excellent performance. |
5 | (1280, 1280) | Large and Accurate: Designed for high-end systems and use cases that require top-notch accuracy. This model has a larger input size of 1280x1280 pixels, resulting in longer training time. |
6 | (1280, 1280) | Deep Learning Powerhouse: This top-of-the-line model provides the best performance and accuracy, with a input size of 1280x1280 pixels. It's the perfect fit for complex projects and advanced users. |
ONNX model inference (onnxruntime>=1.9)
Prepare Input
Onnx Inference
Post-Processing for the output
Output terminology
One detected object: np.array([n_xc, n_yc, n_w, n_h, confidence score, class_idx])
n_xc: normalize box x-center
n_yc: normalize box y-center
n_w: normalize box width
n_h: normalize box height
confidence score
class_idx: class index of training category list
One detection results: array of detected objects
Batch detection results: array of detection results
Post-processing script
Last updated