ALOEv2 is a B-cos-interpretable DINOv3 model obtained by fine-tuning the original ALOE backbones for a further 30k steps. It keeps the original three-layer distillation scheme but adds per-step multi-resolution sampling (224/384/480) and fixes a target-layer bug so the even-thirds distillation layers include the final transformer block.

The original ALOE distilled at a single resolution (224 px), which left features poorly calibrated for the high-resolution inputs that dense-prediction probes — correspondence, depth, surface normals — actually run on, so feature quality degraded at those resolutions. Training on multiple resolutions removes that train/eval mismatch and restores dense-prediction accuracy close to the DINOv3 teacher, while retaining the inherent B-cos explanations and holding ImageNet-1k recognition roughly unchanged.

This card is shared by the ALOEv2 DINOv3 backbones and their ImageNet-1k linear-probe (LP) classifier heads:

Fig 2a — dense correspondence & surface normals across model sizes. ALOEv2 (orange) tracks the DINOv3 teacher closely on NAVI / ScanNet / SPair / surface-normals, while the original ALOE (grey, dotted) lags far behind.

NYUv2 depth probe. δ<1.25 (higher better) and RMSE (lower better) by size:

GridPG localization. ALOEv2 inherent B-cos localization (higher is better):

ImageNet-1k (base): kNN@20 81.43, Linear Probe 83.92.

With output_hidden_states=True, hidden_states[i] is the raw output of block i — in particular hidden_states[-1] is not last_hidden_state, which additionally passes through post_layernorm. This is the convention the distillation loss was defined against, and recent Transformers versions report the post-norm tensor in hidden_states[-1] on the official DINOv3 teacher, so the two APIs differ at that one index.

Use hidden_states[i] for anything layer-wise — ALOEv2 supervises blocks n/3, 2n/3 and n, where its cosine similarity to the DINOv3 teacher's corresponding pre-norm features is 0.98–0.99. post_layernorm was not part of the loss, so last_hidden_state is noticeably less aligned (0.78–0.86).

idx=None explains the predicted class. Pass an ImageNet-1k class index to idx to explain a specific class instead. Do not wrap model.explain(...) in torch.inference_mode(): generating the attribution requires input gradients.

ALOE models use custom B-cos-aware Transformers code, so loading requires trust_remote_code=True. Runtime code: rmaser/aloe-arch.

This model is released under the Creative Commons Attribution-NonCommercial ShareAlike 4.0 International License. The methods described in this work are patent pending.