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README.md

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+---
+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
+
+```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.