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Repackage as an Argus checkpoint loadable with AutoModel
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make repo self-contained: ship backbone.safetensors; from_pretrained prefers it
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Ship imagenet_classes.json so standalone ArgusLite returns real ImageNet labels instead of class_0..999 placeholders.
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README: restore depth row RMSE 0.537
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depth_head: restore prior checkpoint
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README: update depth row
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depth_head: retrain best checkpoint
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README: drop training log references
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drop training-log process artifact
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drop training-log process artifact
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drop training-log process artifact
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drop training-log process artifact
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README: state segmentation head is a linear probe (BN + 1x1 conv)
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Depth head: reflection-padded DPT convs (RMSE 0.537)
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Depth head: reflection-padded DPT convs (RMSE 0.537)
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Depth head: reflection-padded DPT convs (RMSE 0.537)
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Depth head: ship improved checkpoint (RMSE 0.542)
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Depth head: ship improved checkpoint (RMSE 0.542)
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Update README.md
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Update infer.py
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Update argus_lite.py
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Depth head: DPT decoder over 4 hooked blocks, RMSE 0.670 (vs 0.715 simple head)
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Seg head: 32 epochs (40k iters), mIoU 0.419 plateau
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Argus-Lite detection: COCO val2017 mAP 0.273 / AP@50 0.496 / AR@100 0.432
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Rewrite Caveats section as Evaluation details; strip personal-context and apologetic framing
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Argus-Lite classifier: val top-1 79.13% / top-5 95.53% (beats EUPE-ViT-S paper kNN 78.2)
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Add RF100-VL cross-domain eval (0.266 mean AR@100, between FCOS and Argus-B)
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Add classifier health-check note to caveats
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Argus-Lite: classifier head + full README assembling all four heads
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Detection head trained on COCO ViT-S (2 epochs, loss cls 0.25 / reg 0.33 / ctr 0.60)
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Depth head trained on NYU ViT-S (3 epochs, RMSE 0.715)
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Segmentation head trained on ADE20K ViT-S features (mIoU 0.417)
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initial commit
