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@@ -36,6 +36,7 @@ MaskDistill learns visual representations by distilling knowledge from a frozen
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  | Evaluation | Result |
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  |-----------|--------|
 
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  | k-NN (k=10) | **75.6%** top-1 |
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  | Linear Probe | **76.3%** top-1 |
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  | Sem. Seg. (ADE20K, UPerNet) | **52.6** mIoU |
@@ -47,6 +48,7 @@ MaskDistill learns visual representations by distilling knowledge from a frozen
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  | File | Description |
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  |------|------------|
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  | `pretrain_vit_base_ep290.pth` | Pretrained ViT-Base (300 epochs) |
 
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  | `linprobe_vit_base_ep90.pth.tar` | Linear probe (90 epochs, 76.3% top-1) |
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  | `semseg_upernet_ade20k_160k.pth` | UPerNet on ADE20K (52.6 mIoU) |
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  | `detection_maskrcnn_coco_12ep.pth` | Mask R-CNN on COCO (44.4 mAP) |
 
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  | Evaluation | Result |
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  |-----------|--------|
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+ | Finetuning (ImageNet-1K) | **84.8%** top-1 |
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  | k-NN (k=10) | **75.6%** top-1 |
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  | Linear Probe | **76.3%** top-1 |
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  | Sem. Seg. (ADE20K, UPerNet) | **52.6** mIoU |
 
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  | File | Description |
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  |------|------------|
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  | `pretrain_vit_base_ep290.pth` | Pretrained ViT-Base (300 epochs) |
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+ | `finetune_vit_base_ep100.pth` | Finetuned on ImageNet-1K (84.8% top-1) |
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  | `linprobe_vit_base_ep90.pth.tar` | Linear probe (90 epochs, 76.3% top-1) |
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  | `semseg_upernet_ade20k_160k.pth` | UPerNet on ADE20K (52.6 mIoU) |
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  | `detection_maskrcnn_coco_12ep.pth` | Mask R-CNN on COCO (44.4 mAP) |