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README.md

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+---
+library_name: onnx
+pipeline_tag: feature-extraction
+license: mit
+tags:
+- eidora
+- eidora-model-zoo
+- onnx
+- onnxruntime
+- embeddings
+- image
+- clip
+- compute:light
+- modality:image
+base_model: openai/clip-vit-base-patch32
+datasets:
+- openai/webimage-text
+metrics:
+- cosine-similarity
+model-index:
+- name: CLIP_VITB32_512
+  results: []
+---
+
+# CLIP_VITB32_512
+
+CLIP_VITB32_512 is a light image embedding model for fast visual grouping in EIDORA. Choose it when you want a broadly useful visual model that runs on most laptops.
+
+## Best For
+
+- Fast first-pass visual grouping.
+- Image collections where semantic similarity matters more than exact object classification.
+- A broadly useful starter model for EIDORA image workflows.
+
+## Not Ideal For
+
+- Fine-grained visual similarity where a heavier model is acceptable.
+- Text-only projects.
+- Specialized domains that need a domain-trained visual encoder.
+
+## Compute Tier
+
+Light: small download, low memory, faster CPU runtime. Intended for laptop CPU use and large first-pass projects.
+
+## Inputs
+
+- `image`: required image input from `media_source`.
+
+## Output
+
+The primary output is `embedding`, a float32 tensor shaped `[batch, 512]`. Embeddings are already normalized and are intended for cosine similarity.
+
+## Usage In EIDORA
+
+EIDORA shows this package as a light image embedding model in the Model Zoo. Use it for discovery maps, grouping, retrieval, and related embedding workflows.
+
+## Preprocessing
+
+- `image`: resize mode defaults to `crop_center`; rescale uses `1/255`; normalize inside the ONNX graph with mean `[0.48145466, 0.4578275, 0.40821073]` and std `[0.26862954, 0.26130258, 0.27577711]`.
+
+## Authorship And Citation
+
+This ONNX package was produced by EIDORA from the original CLIP ViT-B/32 model. EIDORA converted the model to ONNX and is not the original model creator. Please cite Learning Transferable Visual Models From Natural Language Supervision and the original model repository when using this converted model.
+
+Original model: https://github.com/openai/CLIP
+
+Original paper: https://arxiv.org/abs/2103.00020
+
+Authors: Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever
+
+```bibtex
+@inproceedings{radford2021learning,
+  title={Learning Transferable Visual Models From Natural Language Supervision},
+  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
+  booktitle={International Conference on Machine Learning},
+  year={2021}
+}
+```
+
+## Training Data And Provenance
+
+Base model: `openai/clip-vit-base-patch32`. Source repository: https://huggingface.co/openai/clip-vit-base-patch32. Known training data: WebImageText-style image/text pairs described by the upstream CLIP authors. Package payload size: 351584808 bytes.
+
+## Evaluation And Validation
+
+The package validation checks that the ONNX graph loads with ONNX Runtime CPU execution, runs the declared fixtures, returns finite float32 embeddings with the declared shape, and matches the artifact hash recorded in `config.yaml`.
+
+## Limitations And Safety
+
+CLIP embeddings can reflect web-scale training data biases and may miss fine visual details. Similarity scores should not be used as sole evidence for identity, authorship, intent, or sensitive attributes.
+
+## License And Attribution
+
+This package uses license `mit`. Upstream license: MIT. Converted to ONNX for EIDORA from the original OpenAI CLIP model.
+
+## Version
+
+Package version: 1.0.0. ONNX opset: 17. Exporter: eidora-onnx-exporter 0.1.0.

config.yaml

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+schema_version: 1
+id: EIDORA/CLIP_VITB32
+name: CLIP_VITB32
+version: 1.0.0
+model_family: CLIP
+backend: onnx
+file_size: 352 MB
+runtime:
+  adapter: onnx_image
+  execution_provider: CPUExecutionProvider
+  model_path: model.onnx
+  input_names:
+  - pixel_values
+  output_name: embedding
+  onnxruntime:
+    opset: 17
+    tested_versions: '>=1.17,<2'
+artifact:
+  path: model.onnx
+  sha256: 6e778c76fed3af2e98c837c304fa2f85f545b3e35d13854448c248812fcdf533
+  package_size_bytes: 351584808
+inputs:
+- id: image
+  modality: image
+  label: Images
+  required: true
+  source_kind: media_source
+  requirements:
+    color_space: RGB
+    layout: NCHW
+    width: 224
+    height: 224
+preprocess:
+  image:
+    resize_mode:
+      label: Image preprocessing
+      kind: choice
+      choices:
+      - distort
+      - crop_center
+      - add_padding
+      default: crop_center
+      visible: true
+    resize:
+      mode: resize_shorter_edge_then_center_crop
+      resize_size: 224
+      crop_width: 224
+      crop_height: 224
+      interpolation: bicubic
+      source: CLIPImageProcessor for openai/clip-vit-base-patch32
+    rescale: 1/255
+    normalize:
+      mean:
+      - 0.48145466
+      - 0.4578275
+      - 0.40821073
+      std:
+      - 0.26862954
+      - 0.26130258
+      - 0.27577711
+      inside_onnx: true
+embedding:
+  dimensions: 512
+  feature_type: embedding
+  pooling: pooler
+  normalized: true
+  similarity: cosine
+  output_name: embedding
+  dtype: float32
+  shape:
+  - batch
+  - 512
+display:
+  summary: 'Light: fast image embeddings for visual grouping and discovery on laptops.'
+  compute_tier: light
+  modality_labels:
+  - image
+  recommended_batch_size: 8
+validation:
+  fixtures:
+  - id: image_tensor_001
+    input_shape:
+    - 1
+    - 3
+    - 224
+    - 224
+    expected_shape:
+    - 1
+    - 512
+    seed: 29
+  checks:
+    load_with: onnxruntime
+    execution_provider: CPUExecutionProvider
+    output_dtype: float32
+    finite: true
+    normalized_l2_range:
+    - 0.99
+    - 1.01
+provenance:
+  base_model: openai/clip-vit-base-patch32
+  source_repository: https://huggingface.co/openai/clip-vit-base-patch32
+  original_model_name: CLIP ViT-B/32
+  original_model_url: https://github.com/openai/CLIP
+  authors:
+  - Alec Radford
+  - Jong Wook Kim
+  - Chris Hallacy
+  - Aditya Ramesh
+  - Gabriel Goh
+  - Sandhini Agarwal
+  - Girish Sastry
+  - Amanda Askell
+  - Pamela Mishkin
+  - Jack Clark
+  - Gretchen Krueger
+  - Ilya Sutskever
+  paper_title: Learning Transferable Visual Models From Natural Language Supervision
+  paper_url: https://arxiv.org/abs/2103.00020
+  upstream_license: MIT
+  training_data: WebImageText-style image/text pairs described by the upstream CLIP
+    authors.
+  citation: "@inproceedings{radford2021learning,\n  title={Learning Transferable Visual\
+    \ Models From Natural Language Supervision},\n  author={Radford, Alec and Kim,\
+    \ Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal,\
+    \ Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark,\
+    \ Jack and Krueger, Gretchen and Sutskever, Ilya},\n  booktitle={International\
+    \ Conference on Machine Learning},\n  year={2021}\n}\n"
+  conversion_note: EIDORA produced this ONNX conversion and is not the original model
+    creator.
+  export_date: '2026-07-14'
+  exporter_version: eidora-onnx-exporter 0.1.0
+model_card:
+  best_for:
+  - Fast first-pass visual grouping.
+  - Image collections where semantic similarity matters more than exact object classification.
+  - A broadly useful starter model for EIDORA image workflows.
+  not_ideal_for:
+  - Fine-grained visual similarity where a heavier model is acceptable.
+  - Text-only projects.
+  - Specialized domains that need a domain-trained visual encoder.
+  limitations: CLIP embeddings can reflect web-scale training data biases and may
+    miss fine visual details. Similarity scores should not be used as sole evidence
+    for identity, authorship, intent, or sensitive attributes.
+  license:
+    id: mit
+    attribution: Converted to ONNX for EIDORA from the original OpenAI CLIP model.
+  huggingface:
+    org: eidora
+    repo_name: CLIP_VITB32_512
+    pipeline_tag: feature-extraction
+    tags:
+    - eidora
+    - eidora-model-zoo
+    - onnx
+    - onnxruntime
+    - embeddings
+    - image
+    - clip
+    - compute:light
+    - modality:image
+    datasets:
+    - openai/webimage-text
+    metrics:
+    - cosine-similarity

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e778c76fed3af2e98c837c304fa2f85f545b3e35d13854448c248812fcdf533
+size 351584308