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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:

Download Steps

# step1: connect to your dataverse host
from dataverse_sdk import *
from dataverse_sdk.connections import get_connection
client = DataverseClient(
host=DataverseHost.DEMO, email="XXX", password="***"
)
assert client is get_connection()
# step2: get project and model list
project = client.get_project(project_id=1)
models = project.list_models()
# step3: get your model by model_id
model = project.get_model(model_id=30)
# step4: Download labels.txt and model.onnx
status, label_file_path = model.get_label_file(save_path="./labels.txt", timeout=6000)
status, onnx_model_path = model.get_onnx_model_file(save_path="./model.onnx", timeout=6000)

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

import cv2
import numpy as np
image_path_list = ['path/img1.jpg', 'path/img2.jpg']
image_input = []
for image_path in image_path_list:
img = cv2.imread(image_path) #bgr image
#resize image as the input size of your selected model_code (model 1~6)
img_size = cv2.resize(img, (640, 640), interpolation=cv2.INTER_AREA)
image_input.append(img_size)
inputs = {"INPUT": np.array(image_input)}
output_names = ["OUTPUT"]

Onnx Inference

import onnxruntime as ort
onnx_file = "/file_path_to/model.onnx"
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
# If there is a kernel in the CUDA execution provider ONNX Runtime executes that on GPU.
# If not the kernel is executed on CPU.
sess = ort.InferenceSession(str(onnx_file), providers= providers)
outputs = sess.run(output_names, inputs)

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

# Load category
with open("/path/labels.txt") as f:
category = f.read()
class_list = category.split("\n")
img_w = 1280 # modify as your original image width
img_h = 720 # modify as your original image height
prediction_results = outputs[0]
for img_results in prediction_results:
for obj in img_results:
category = class_list[ int(obj[5])]
confidence_score = obj[4]
x = int(obj[0] * img_w)
y = int(obj[1] * img_h)
w = int(obj[2] * img_w)
h = int(obj[3] * img_h)
# TODO: save the results as your target format