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| Index | Class |
|---|---|
| 0 | paper |
| 1 | plastic |
| 2 | metal |
| 3 | organic |
| 4 | other |
1import onnxruntime
2import numpy as np
3from PIL import Image
4
5session = onnxruntime.InferenceSession("best.onnx")
6input_name = session.get_inputs()[0].name
7# Input: [1, 3, 640, 640] normalized float32
8# Output: [1, 9, 8400] = [cx, cy, w, h, p0, p1, p2, p3, p4]