ConvNeXt-V2-L · Live Inference
Real forward + backward passes on CPU: predictions, Grad-CAM, and robustness probes.
analytics96.5% orig · 96.9% cleanWeapons · 9-class198M paramsIn this workspace:Grad-CAMFGSM attackCorruptionsOOD signal
Recognizes9 weapon categories9 classes · bazooka, grenade launcher, rifle, shotgun, smg, sniper, knife, sword, handgun
Accuracy96.5% orig · 96.9% clean96.5% on the original split, 96.9% on the clean (leakage-proof) split
Works best onA photo of one of the nine weapon typesResized to 224×224 color, ImageNet-normalized.
warning
Expect confident mistakes outside its scope
This model only knows nine weapon classes and has no "no weapon" option. Shown an ordinary object — an animal, a vehicle, a face — it will still return a weapon label, sometimes confidently. We measured this directly: 27 of 27 non-weapons were labelled as weapons. Treat the confidence value, not just the label, as the signal.
Top-5 Predictions
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Input
Activation Map
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Focus Metric
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Latency
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Confidence (top-1)
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Inference Latency
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CPU forward pass