Publish VideoMAE Base ONNX - CC BY-NC 4.0

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README.md

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+---
+license: cc-by-nc-4.0
+base_model: MCG-NJU/videomae-base
+pipeline_tag: feature-extraction
+tags:
+  - eidora
+  - eidora-model-zoo
+  - onnx
+  - onnxruntime
+  - embeddings
+  - video
+  - videomae
+  - non-commercial
+---
+
+# VideoMAE_Base_K400
+
+VideoMAE Base is a self-supervised video transformer pretrained on Kinetics-400. This EIDORA package converts the pretrained encoder into a 768-dimensional normalized clip embedding.
+
+## Source and provenance
+
+- Base model/checkpoint: `MCG-NJU/videomae-base`
+- Source: https://huggingface.co/MCG-NJU/videomae-base
+- Original model: VideoMAE Base
+- Architecture paper: https://proceedings.neurips.cc/paper_files/paper/2022/hash/416f9cb3276121c42eebb86352a4354a-Abstract-Conference.html
+- Training data: Kinetics-400, self-supervised pretraining for 1600 epochs
+
+## EIDORA package
+
+- Package schema: `3` (Desktop preparation implementation `1`)
+- Declared providers: `CPUExecutionProvider`
+- Provider declarations require certification in validation-report.json before publication.
+- Preparation runs in EIDORA; exposed defaults are recommended, alternatives are not equivalent-quality claims.
+- Modality: `video`
+- Embedding dimensions: `768`
+- Normalized: `true`
+- Similarity: `cosine`
+
+## Preparation
+
+```yaml
+width: 224
+height: 224
+layout: BTCHW
+resize: short_edge_crop
+interpolation: bilinear
+rescale: 0.00392156862745098
+mean:
+- 0.0
+- 0.0
+- 0.0
+std:
+- 1.0
+- 1.0
+- 1.0
+alpha_background:
+- 0
+- 0
+- 0
+short_edge: 224
+frame_count: 16
+duration_seconds: 2.0
+excerpt: beginning
+parameters:
+  resize:
+    default: short_edge_crop
+    choices:
+    - stretch
+    - fill_crop
+    - fit_pad
+    - short_edge_crop
+  interpolation:
+    default: bilinear
+    choices:
+    - nearest
+    - bilinear
+    - bicubic
+    - lanczos
+  crop_position:
+    default: center
+  short_edge:
+    default: 224
+    minimum: 224
+    maximum: 2048
+  excerpt:
+    default: beginning
+  start_seconds:
+    default: 0.0
+    maximum: 604800
+  duration_seconds:
+    default: 2.0
+    minimum: 0.1
+    maximum: 60
+```
+
+## Intended use
+
+- Clip-level video similarity and retrieval.
+- Clustering video clips using both spatial and temporal visual information.
+- Comparing motion-aware video representations with frame-based image embeddings.
+- Exploratory analysis of video collections where text alignment is not required.
+
+## Limitations
+
+This checkpoint was pretrained on Kinetics-400 and therefore reflects the visual and action distribution of that dataset. It has no audio or language alignment. EIDORA converts the encoder output into one 768-dimensional embedding by mean-pooling the final spatiotemporal tokens and applying L2 normalization. The ONNX model accepts exactly 16 RGB frames at 224 x 224; decoding, temporal frame selection, resizing, cropping, and rescaling are performed outside the graph. The upstream checkpoint is licensed CC-BY-NC-4.0, so its non-commercial restriction must be considered before redistribution or use.
+
+## License and attribution
+
+Based on the VideoMAE Base checkpoint released by MCG-NJU. The upstream checkpoint is licensed under CC-BY-NC-4.0.
+
+## Citation
+
+```bibtex
+@inproceedings{tong2022videomae,
+  title={VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training},
+  author={Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin},
+  booktitle={Advances in Neural Information Processing Systems},
+  volume={35},
+  pages={10078--10093},
+  year={2022},
+  doi={10.52202/068431-0732}
+}
+```
+
+
+## Available EIDORA input modes
+
+**This package supports video only.**
+
+It accepts a 16-frame video clip and generates one normalized,
+768-dimensional embedding.
+
+It does not process audio, accept text queries, perform video
+captioning, or provide a shared text-video embedding space.
+
+There is currently no separately validated image-only,
+text-only, or multimodal variant of this VideoMAE checkpoint
+in EIDORA.
+
+## EIDORA adaptation
+
+The original VideoMAE pretrained encoder is retained.
+EIDORA obtains clip-level embeddings by averaging the final
+spatiotemporal token representations and applying L2
+normalization.
+
+These embeddings are intended for exploratory video similarity,
+retrieval and clustering.
+
+## License and attribution
+
+**License: CC BY-NC 4.0 — non-commercial use only.**
+
+Original checkpoint:
+https://huggingface.co/MCG-NJU/videomae-base
+
+Original project:
+https://github.com/MCG-NJU/VideoMAE
+
+This ONNX conversion does not remove the original checkpoint's
+non-commercial restrictions.
+
+See the LICENSE file included in this repository.
+
+VideoMAE:
+Tong et al., *VideoMAE: Masked Autoencoders are Data-Efficient
+Learners for Self-Supervised Video Pre-Training*, NeurIPS 2022.
+
+## Validation and distribution
+
+The ONNX model was validated against the original PyTorch
+encoder and the EIDORA schema-v3 runtime.
+
+Real-video validation evidence is retained in the EIDORA
+development package. Validation media and diagnostic tensors
+are not included in this public inference distribution.

config.yaml

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+schema_version: 3
+id: EIDORA/VideoMAE_Base_K400
+name: VideoMAE_Base_K400
+version: 0.1.0
+modality: video
+license: cc-by-nc-4.0
+bibtex: "@inproceedings{tong2022videomae,\n  title={VideoMAE: Masked Autoencoders\
+  \ are Data-Efficient Learners for Self-Supervised Video Pre-Training},\n  author={Tong,\
+  \ Zhan and Song, Yibing and Wang, Jue and Wang, Limin},\n  booktitle={Advances in\
+  \ Neural Information Processing Systems},\n  volume={35},\n  pages={10078--10093},\n\
+  \  year={2022},\n  doi={10.52202/068431-0732}\n}"
+compatibility:
+  min_eidora_version: 0.1.0
+artifacts:
+- path: model.onnx
+  sha256: 7107a1ae23a38882335ee7cb75caa0009a6ea786ea5304b46ce6aff72cb1f17e
+  role: graph
+runtime:
+  graph: model.onnx
+  inputs:
+  - name: pixel_values
+    source: frames
+    dtype: float32
+    shape:
+    - batch
+    - 16
+    - 3
+    - 224
+    - 224
+  output:
+    name: embedding
+    dtype: float32
+    shape:
+    - batch
+    - 768
+  execution_providers:
+  - CPUExecutionProvider
+preparation:
+  width: 224
+  height: 224
+  layout: BTCHW
+  resize: short_edge_crop
+  interpolation: bilinear
+  rescale: 0.00392156862745098
+  mean:
+  - 0.0
+  - 0.0
+  - 0.0
+  std:
+  - 1.0
+  - 1.0
+  - 1.0
+  alpha_background:
+  - 0
+  - 0
+  - 0
+  short_edge: 224
+  frame_count: 16
+  duration_seconds: 2.0
+  excerpt: beginning
+  parameters:
+    resize:
+      default: short_edge_crop
+      choices:
+      - stretch
+      - fill_crop
+      - fit_pad
+      - short_edge_crop
+    interpolation:
+      default: bilinear
+      choices:
+      - nearest
+      - bilinear
+      - bicubic
+      - lanczos
+    crop_position:
+      default: center
+    short_edge:
+      default: 224
+      minimum: 224
+      maximum: 2048
+    excerpt:
+      default: beginning
+    start_seconds:
+      default: 0.0
+      maximum: 604800
+    duration_seconds:
+      default: 2.0
+      minimum: 0.1
+      maximum: 60
+embedding:
+  dimensions: 768
+  normalized: true
+  similarity: cosine
+metadata:
+  summary: VideoMAE Base video embeddings for clip-level visual similarity, retrieval,
+    clustering, and temporal representation analysis.
+  trained_on: Kinetics-400, self-supervised pretraining for 1600 epochs
+  feature_type: embedding
+  pooling: mean of final-layer VideoMAE spatiotemporal tokens
+  group:
+    id: video
+    label: Video Models
+    order: 30
+  provenance:
+    base_model: MCG-NJU/videomae-base
+    source_repository: https://huggingface.co/MCG-NJU/videomae-base
+    original_name: VideoMAE Base
+    original_model_url: https://github.com/MCG-NJU/VideoMAE
+    paper_title: 'VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised
+      Video Pre-Training'
+    paper_url: https://proceedings.neurips.cc/paper_files/paper/2022/hash/416f9cb3276121c42eebb86352a4354a-Abstract-Conference.html
+    training_data: Kinetics-400, self-supervised pretraining for 1600 epochs
+    upstream_license: CC-BY-NC-4.0
+    authors:
+    - Zhan Tong
+    - Yibing Song
+    - Jue Wang
+    - Limin Wang
+    source_model: MCG-NJU/videomae-base
+    source_revision: dc740ceda42fce44faed2ea03c6d447db72f6af9
+    source_weights_sha256: bc053ca2840a038b1068269a4eec06ca569689e9a1ed9376a5b2b8a111be5290
+    transformers_version: 4.57.1
+    exported_opset: 17
+    export_date: '2026-10-26'
+    exporter_version: eidora-onnx 1.0.0

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 349969601