YOLOv5m — Person Detector (ONNX)

ONNX export of YOLOv5m, used as the person detector in the PULAO event access-control vision pipeline (person boxes feed a ByteTrack tracker + ArcFace face recognition).

⚠️ Provenance / license. This appears to be a standard Ultralytics YOLOv5m model (COCO-pretrained), exported to ONNX from PyTorch. Ultralytics YOLOv5 is licensed AGPL-3.0. If you redistribute or deploy this you must comply with AGPL-3.0 (retain the license, make corresponding source available); a commercial Ultralytics license, if you hold one, governs instead. Credit: Ultralytics YOLOv5.

Files

yolov5m_dynamic.onnx — 84.7 MB
SHA-256: 2bebc77005d6946d0e81e0a1abab3c849b7caa0ced162d0aae89d52d8b4e7e01

Inputs / outputs

Input images: float32 [N, 3, H, W] (dynamic spatial dims). In this pipeline each frame is letterboxed to a square and resized to 320×320, scaled by 1/255, channels swapped to RGB.
Output: YOLOv5 detection tensor [1, num_boxes, 85] = [cx, cy, w, h, obj, 80×class_scores] (COCO classes). The pipeline keeps the person class, then applies NMS.

Usage (onnxruntime)

import cv2, onnxruntime as ort
sess = ort.InferenceSession("yolov5m_dynamic.onnx", providers=["CPUExecutionProvider"])
img = cv2.imread("frame.jpg")
blob = cv2.dnn.blobFromImage(img, 1/255, (320, 320), swapRB=True, crop=False)
out = sess.run(None, {"images": blob})[0]   # [1, N, 85]

Intended use

Person detection for an event check-in / access-control prototype. Research / prototype use.

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