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PDM PAE DINOv2-L d32 ImageNet-256 Train Latent Cache
This repository contains the PAE latent cache produced for /workspace/PDM.
Contents
- Cache type: PAE DINOv2-L d32 latent cache
- Source split: ImageNet-1k train after 256x256 ADM-style cropping
- Local source path during creation:
/workspace/PDM/data/pae_latents/PAE_DINOv2L_d32/imagenet256_train_full - Number of shards: 313
- Total samples: 1,281,167
- Total size: 41.992 GB decimal
- Latent tensor shape per sample: [32, 16, 16]
- File format: safetensors
- Shard keys: labels, latents, latents_flip
Data files are under:
pae_latents/imagenet256_train_full/latents_rank00_shard*.safetensors
Additional metadata:
cache_summary.json: summary generated before uploadmanifest.jsonl: one JSON record per shard with size/sample/shape metadata
Provenance
- PAE repo commit:
51f8fa6290e6c1f4ffb9bb9aea500bfb707ffd12 - PAE config:
PAE_DINOv2L_d32.yaml - Expected total samples from local full-cache scan:
1,281,167 - Local full-cache scan status before upload:
ready_for_full_train = true - Local smoke4096 status before upload:
practical_gate_pass = true
Loading example
from safetensors import safe_open
path = "pae_latents/imagenet256_train_full/latents_rank00_shard000000.safetensors"
with safe_open(path, framework="pt", device="cpu") as f:
labels = f.get_tensor("labels")
latents = f.get_tensor("latents")
latents_flip = f.get_tensor("latents_flip")
print(labels.shape, latents.shape, latents_flip.shape)
License / upstream data note
This is a derived latent cache from ImageNet-1k training images. Use of this cache should comply with the upstream ImageNet terms and any applicable model/checkpoint terms. This repository does not include the raw ImageNet images.
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