| --- |
| license: other |
| license_name: imagenet-derived-research-only |
| license_link: https://www.image-net.org/download.php |
| viewer: false |
| pretty_name: "ImageNet backbone features (fp32) for SAE training" |
| tags: |
| - embeddings |
| - features |
| - imagenet |
| - sparse-autoencoder |
| extra_gated_heading: "Request access to these features" |
| extra_gated_description: "Access requests are reviewed manually by the authors. Expect a few days for processing." |
| extra_gated_prompt: | |
| These features are derived from ImageNet-1k. By requesting access, you agree to: |
| 1. Comply with the ImageNet terms of access — non-commercial research and education only. |
| 2. Not redistribute the features, in whole or in part, to any third party. |
| 3. Cite both ImageNet and the associated work in any publication that uses them. |
| extra_gated_fields: |
| Full name: text |
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| Country: country |
| Intended use: |
| type: select |
| options: |
| - Academic research |
| - Evaluation on an interpretability platform |
| - label: Other |
| value: other |
| Describe your intended use in one sentence: text |
| I have accepted the ImageNet terms of access: checkbox |
| I agree not to redistribute these features: checkbox |
| extra_gated_button_content: "Request access" |
| --- |
| |
| # ImageNet backbone features (fp32) |
|
|
| Activations pré-extraites d'ImageNet-1k pour trois backbones, mémoire-mappables, |
| utilisées pour entraîner les SAE du repo `SAE_CBM_unification`. |
|
|
| ## Contenu |
|
|
| 136 shards `.npy`, 37.2 Go, fp32. |
|
|
| | Backbone | Node | Split | Shards | Dim | Taille | |
| | --- | --- | --- | --- | --- | --- | |
| | `resnet50` | `avgpool` | train / val | 14 / 2 | 2048 | 9.8 Go / 392 Mo | |
| | `resnet50` | `layer1` | train / val | 16 / 4 | — | 1.3 Go / 51 Mo | |
| | `resnet50` | `layer2` | train / val | 16 / 4 | — | 2.5 Go / 100 Mo | |
| | `resnet50` | `layer3` | train / val | 16 / 4 | — | 5.0 Go / 198 Mo | |
| | `resnet50` | `layer4` | train / val | 16 / 4 | — | 9.9 Go / 393 Mo | |
| | `vit_b_16` | `penultimate` | train / val | 16 / 4 | 768 | 3.8 Go / 149 Mo | |
| | `dinov2_vitb14_reg` | `penultimate` | train / val | 16 / 4 | 768 | 3.8 Go / 149 Mo | |
|
|
| ``` |
| <backbone>/<node>/<split>/<split>_000NN.npy # shards contigus, fp32 |
| concept_vocab_thr0500.json # vocabulaire concepts, seuil 500 |
| concept_vocab_thr5000.json # vocabulaire concepts, seuil 5000 |
| ``` |
|
|
| Un shard `train` est de forme `[100000, D]`, un shard `val` de forme `[50000, D]`. |
| Les shards sont **ordonnés** : concaténés dans l'ordre lexicographique des noms, ils |
| reconstituent le split complet dans l'ordre d'origine du DataLoader. |
|
|
| ## Chargement |
|
|
| ```python |
| import numpy as np, glob |
| shards = sorted(glob.glob("resnet50/avgpool/train/train_*.npy")) |
| X = np.concatenate([np.load(p, mmap_mode="r") for p in shards]) # ou lire shard par shard |
| ``` |
|
|
| Le pooling est un GAP (`--pool gap`), sans projection (`--proj-dim 0`), extrait en fp32. |
|
|
| ## Provenance |
|
|
| Extraites avec `extract_features_fp32_blocks_imn_VIT.py` (inclus dans le repo |
| `SAE_CBM_unification`) depuis ImageNet-1k train/val. Le pipeline SAE ne lit jamais |
| les images brutes : il consomme directement ces shards mémoire-mappés. |
|
|
| ## Licence |
|
|
| Features **dérivées d'ImageNet-1k**. La redistribution est soumise aux conditions |
| d'accès d'ImageNet : usage recherche non commercial. L'accès est gaté pour cette |
| raison — voir le formulaire ci-dessus. |
|
|
| ## Citation |
|
|
| TODO — référence de l'article + citation ImageNet (Deng et al., 2009). |
|
|
