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binxu/spatial-neural-feature-accentuation-checkpoints

sourceHugging Faceotherupdated 29d agoView on Hugging Face

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Spatial Neural Feature Accentuation checkpoints

Runtime artifacts for Animadversio/spatial-neural-feature-accentuation.

Files

File	Purpose	SHA-256
`resnet50_robust_backbone.pt`	Adversarially robust ImageNet ResNet-50 state dict	`6c6731b622d6e521d4e36707f5a0d24d18ff7d8ffee1ac30b7a68eb36871c763`
`resnet50_robust_25_compiled_targets.pt`	25 PCA/readout objectives collapsed to feature-space weights and biases	`69975cfdf5abaaecfaf34ea76d05b02f93bc2d4f13a195d99b3652a1f7b24757`

The compiled cache contains five selected neural-encoding targets for each of five monkeys (leap, paul, red, three0, and venus). Internal filesystem paths have been removed from this publication copy. It retains target IDs, subject labels, unit IDs, robust-ResNet layer names, effective weights/biases, and q01/q99 response normalization values.

Use

The companion repository downloads these files at a pinned Hub revision and checks both SHA-256 hashes before loading them. They can also be downloaded with:

bash

hf download binxu/spatial-neural-feature-accentuation-checkpoints \
  --include 'resnet50_robust_*.pt' \
  --local-dir checkpoints

These files are intended for differentiable feature visualization and the research-art workflow documented in the companion repository. The 25 compiled targets are not general-purpose image classifiers.

Licenses and provenance

The companion code is MIT licensed. These weight artifacts retain the terms of their original models and source data; users are responsible for complying with those terms. See the companion repository for method details, limitations, privacy guidance, target definitions, and reproducibility metadata.