README.md

metadata

library_name: onnx
pipeline_tag: video-classification
tags:
  - onnx
  - video-classification
  - accident-detection
  - dashcam
  - jetson
  - rockchip
license: mit

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

{
  "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

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.