Compressed video data made the AI model more robust, not less. Beamr fine-tuned Depth Anything V2 on AV footage compressed with content-adaptive compression (patented CABR). The result: -> 35.2% smaller than baseline -> 30.7% lower depth estimation error on safety-critical objects, like pedestrians and motorcyclists (VRUs) -> 16.0% lower error, aggregated The industry has treated compression as a tradeoff. CABR-compressed footage made the model more resilient to compression, while freeing storage, networking, and infrastructure capacity. Compression became an asset. Press release: shorturl.at/De9GW For full methodology and results: shorturl.at/Biwzk #Beamr #Video #Data #MLSafe #PhysicalAI

If you're managing petabyte-scale AV video data, compression is no longer optional. Without it, storage, egress, I/O time, and pipeline throughput all compound. But compression introduces a critical question the industry hasn't systematically answered: how do you confirm ML model integrity across every pipeline stage? ADAS & Autonomous Vehicle International featured Beamr's ML-safe compression framework, benchmarked across the AV pipeline: ♢ Up to 50% file size reduction ♢ Precision (mAP) remains consistently high ♢ Localization differences minimal ♢ Confidence scores remain highly correlated ♢ Visual Realism Index (VRI): 93%–98% agreement -> Read the full article in ADAS & Autonomous Vehicle International: shorturl.at/5Z8xe -> Run Beamr's ML-safe compression on your own data: beamr.com/autonomous #Beamr #AutonomousVehicles #MLSafe #Video #Data #PhysicalAI

You guys need to be real and bring some high profile players too. All of the other MLSafe making these efforts or have done it, except the New England Revolution