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akhra92/dashcam-collision-jetson-videomae-hnm

sourceHugging Facemitupdated 3mo agoView on Hugging Face

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Dashcam Collision Detector (videomae_base, ONNX)

Causal sliding-window crash detector exported to ONNX. The model scores a short temporal window of dashcam frames and a downstream rule (consec consecutive detections above detect_threshold) decides when a collision occurs.

—Architecture: videomae_base
—Input: float32 [1, 3, 16, 224, 224] (NCTHW), RGB, ImageNet mean/std normalized
—Sampling: 16-frame window at 16 fps
—Decision rule: threshold 0.97, 3 consecutive windows

Inference metadata

json

{
  "input_shape": [
    1,
    3,
    16,
    224,
    224
  ],
  "detect_threshold": 0.97,
  "consec": 3,
  "target_fps": 16,
  "window_frames": 16,
  "stride": 3,
  "tolerance_s": 1.0,
  "mean": [
    0.485,
    0.456,
    0.406
  ],
  "std": [
    0.229,
    0.224,
    0.225
  ],
  "arch": "videomae_base"
}

Usage

python

from huggingface_hub import hf_hub_download
import onnxruntime as ort, numpy as np

path = hf_hub_download(repo_id="akhra92/dashcam-collision-jetson-videomae-hnm", filename="model.onnx")
sess = ort.InferenceSession(path, providers=["CPUExecutionProvider"])
x = np.random.randn(*[1, 3, 16, 224, 224]).astype("float32")
logit = sess.run(["logit"], {"input": x})[0]

On-device, the ONNX graph is compiled to a TensorRT engine (Jetson) or an .rknn model (Rockchip). See deploy/ in the source repo.