| 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 |
|
|
