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- # RAEv2-artifacts
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-
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- Pre-computed generation NPZs from RAEv2 stage-2 models. Each `*.npz` is
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- 50000 x 256 x 256 x 3 `uint8` NHWC RGB samples under key `arr_0`.
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-
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- | file | task | encoder | gFID |
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- |---|---|---|---:|
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- | `imagenet-dinov3l-k7.npz` | ImageNet 256, label-conditional | DINOv3-L k=7 (MLS last 7 layers) | 1.025 |
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-
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- ## Usage
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-
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- Compute the full metric suite (`gfid`, `fdr6`, `mind6`, ...) without
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- re-running distributed sampling:
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-
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- ```bash
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- hf download nanovisionx/RAEv2-artifacts --repo-type dataset \
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- --include imagenet-dinov3l-k7.npz --local-dir artifacts/
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-
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- EXPERIMENT_NAME=imagenet-dinov3l-k7 uv run python src/offline_eval.py \
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- --config configs/stage2/sampling/imagenet-dinov3l-k7.yaml \
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- --npz artifacts/imagenet-dinov3l-k7.npz
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- ```
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-
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- See the [main repo](https://github.com/nanovisionx/RAEv2) for the model code
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- and yaml configs.