Complete feature dataset release after file and SHA-256 verification

FILES.json

@@ -30,8 +30,8 @@
     },
     {
       "path": "README.md",
-      "bytes": 33418,
-      "sha256": "4da2e01eb97e74f6d145947bba789d2d8c593254425838d844bf8d1f828327ee"
+      "bytes": 33584,
+      "sha256": "992348fa8657643973da4cae6306632445d1779498e3a9a2955b721aa9c4daad"
     },
     {
       "path": "encoders.json",
@@ -2470,7 +2470,7 @@
     }
   ],
   "total_files": 302,
-  "total_bytes": 123709313038,
+  "total_bytes": 123709313204,
   "source_lcs_manifest_sha256": "bc439048824c510b471d4c8cf34b143471a49a50260906f210cb8a1723e971ed",
   "published_lcs_manifest_sha256": "f934b05aadf9b94039b207812033e48b1e2aefe43e0fd084b5508e6fd103dbed",
   "validation": {
@@ -2482,6 +2482,6 @@
     "knn_shot_subsets_nested": true,
     "uniar_repaired_array_hash_verified": true
   },
-  "release_status": "upload_in_progress",
+  "release_status": "complete",
   "inventory_note": "FILES.json and checksums.sha256 are excluded from the listed file count/hashes to avoid circular hashes."
 }

README.md

@@ -52,7 +52,7 @@ This dataset provides cached representations for the **70 vision encoders / visu
 
 The release contains LCS-558K image patch features for RAVEL and other paired image–text probes, three matching language-model text feature arrays, and ImageNet-1K pooled image features for kNN and linear probes. Each encoder has its own metadata. The original NumPy arrays and storage precision are retained.
 
-**Release status:** **Upload in progress.** The full expected inventory is in `FILES.json`; feature files become available as batches are committed.
+**Release status:** **Complete.** All release file paths, byte sizes, and SHA-256 values have been verified against the Hub.
 
 **Resources:** [Paper](https://arxiv.org/abs/2610.05413) · [Evaluation code](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval) · [MLLM Model Zoo and encoder weights](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) · [Encoder catalog](encoders.json) · [File inventory](FILES.json)
 
@@ -123,7 +123,7 @@ The full manifest additionally retains the source conversation records and sampl
 
 ### ImageNet-1K: 200,000 training examples
 
-The subset contains **200 examples per class**, across **1,000 classes**, from the official ImageNet-1K training split. All 70 arrays have shape **`[200000, D]`** and are stored as **float32**. Shared `source_indices.npy` is strictly increasing in the original train-index order; `labels.npy` contains integer class IDs `0–999`.
+The subset contains **200 examples per class**, across **1,000 classes**, from the official ImageNet-1K training split. The export records a **support-sampling seed of 0**; the exact selected examples are given by `source_indices.npy`. All 70 arrays have shape **`[200000, D]`** and are stored as **float32**. Shared `source_indices.npy` is strictly increasing in the original train-index order; `labels.npy` contains integer class IDs `0–999`.
 
 The sample table contains `row_index`, `source_index`, and `label`. Each array's readout is recorded in its metadata: for example, CLIP uses its final post-LayerNorm CLS before the visual projection, while the DINO readout concatenates normalized CLS and mean patch features. **Use the per-encoder representation description when interpreting dimensions.** These pooled/readout arrays have different semantics from the LCS patch-token arrays.
 
@@ -299,7 +299,7 @@ The dataset inventory records array shapes, dtypes, byte sizes, SHA-256 hashes,
 - **Historical patch provenance:** the original visual audits do not record a uniform feature-layer specification. Missing extraction-layer, processor, or weight-revision fields remain explicitly unverified in this release. The arrays should be interpreted using available per-encoder metadata and the extraction code, rather than assuming a common layer/readout.
 - **UniAR correction:** the published UniAR array uses the documented legacy BSQ deepstack concatenation. One NaN at `[630, 295, 1051]` was replaced by `1.7109375`, obtained by re-extraction with the official model. Its metadata retains original/repaired hashes, pinned model/source revisions, weight hash, and verification of the remaining 1,151 components in that BSQ vector.
 - **Coverage and bias:** LCS-1000 is a small captioned-image sample; ImageNet200k is a class-balanced training subset. Findings depend on these data sources and selected encoders. Features inherit the content and representation biases of the upstream datasets and models.
-- **Scope:** raw images, complete encoder weights, MLLM checkpoints, PCA/distance caches, and larger-sample feature experiments are available through their respective upstream resources or project releases.
+- **Scope:** this release contains the two feature subsets described above. Obtain raw images and encoder weights from the upstream sources, and MLLM checkpoints from the Model Zoo. Derived PCA/distance caches and larger-sample feature experiments are outside the current release.
 
 ## Licensing and Attribution
 

checksums.sha256

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