模型下載 (Beta)

模型下載

Dataverse (beta) 提供了 ONNX FP16 版本下載和簡易的 inference 操作方式。

Hint

您可以在 Model 頁面上下載訓練好的AI模型。

快速開始

參考實際操作影片：

ONNX 模型 inference 辦法 (onnxruntime>=1.9)

準備 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
    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