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README.md

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+---
+library_name: onnx
+pipeline_tag: feature-extraction
+license: bsd-3-clause
+tags:
+- eidora
+- eidora-model-zoo
+- onnx
+- onnxruntime
+- embeddings
+- image
+- alexnet
+- imagenet
+- compute:light
+- modality:image
+base_model: torchvision/alexnet-imagenet1k-v1
+datasets:
+- imagenet-1k
+metrics:
+- cosine-similarity
+model-index:
+- name: alexnet_imagenet1k_4096
+  results: []
+---
+
+# alexnet_imagenet1k_4096
+
+alexnet_imagenet1k_4096 is a light image embedding model for fast first-pass visual grouping in EIDORA. Choose it when you want a small, classic baseline that runs comfortably on ordinary laptops.
+
+## Best For
+
+- Fast baseline visual grouping.
+- Small or exploratory image projects on ordinary laptops.
+- Regression testing the EIDORA ONNX package pipeline.
+
+## Not Ideal For
+
+- Fine-grained visual similarity where modern self-supervised models perform better.
+- Text, video, or audio inputs.
+- Production-quality semantic image retrieval when a stronger model is acceptable.
+
+## 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, 4096]`. 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.
+
+## Usage Examples
+
+Download the complete package from Hugging Face and keep the generated files together:
+
+```python
+from huggingface_hub import snapshot_download
+
+package_dir = snapshot_download("eidora/alexnet_imagenet1k_4096")
+# EIDORA reads config.yaml, README.md, and model.onnx from this folder.
+```
+
+## Preprocessing
+
+- `image`: rescale uses `1/255`; normalize inside the ONNX graph with mean `[0.485, 0.456, 0.406]` and std `[0.229, 0.224, 0.225]`.
+
+## Authorship And Citation
+
+This ONNX package was produced by EIDORA from the original AlexNet ImageNet model. EIDORA converted the model to ONNX and is not the original model creator. Please cite ImageNet Classification with Deep Convolutional Neural Networks and the original model repository when using this converted model.
+
+Original model: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
+
+Original paper: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
+
+Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
+
+```bibtex
+@inproceedings{krizhevsky2012imagenet,
+  title={ImageNet Classification with Deep Convolutional Neural Networks},
+  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2012}
+}
+```
+
+## Training Data And Provenance
+
+Base model: `torchvision/alexnet-imagenet1k-v1`. Source repository: https://pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html. Known training data: ImageNet-1K supervised classification data. Package payload size: 228020495 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 `eidora-model.yaml`.
+
+## Limitations And Safety
+
+AlexNet is an older supervised ImageNet model. It is useful as a lightweight baseline, but modern CLIP, DINOv2, SigLIP, or domain-specific models will usually produce stronger semantic similarity.
+
+## License And Attribution
+
+This package uses license `bsd-3-clause`. Upstream license: BSD-3-Clause for TorchVision code; ImageNet-trained weights distributed by PyTorch under their documented model terms. Converted to ONNX for EIDORA from the TorchVision AlexNet ImageNet weights.
+
+## 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/AlexNet_IN1k
+name: AlexNet_IN1k
+version: 1.0.0
+model_family: AlexNet
+backend: onnx
+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: 3e1277374f9da12a0c92084e19003dd541a792fccd5b4c0fd2bc4bfa54b28cc6
+  package_size_bytes: 228020495
+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: resize_shorter_edge_then_center_crop
+      resize_size: 256
+      crop_width: 224
+      crop_height: 224
+      interpolation: bilinear
+      source: TorchVision AlexNet_Weights.IMAGENET1K_V1.transforms
+    rescale: 1/255
+    normalize:
+      mean:
+      - 0.485
+      - 0.456
+      - 0.406
+      std:
+      - 0.229
+      - 0.224
+      - 0.225
+      inside_onnx: true
+embedding:
+  dimensions: 4096
+  feature_type: embedding
+  pooling: fc_head
+  normalized: true
+  similarity: cosine
+  output_name: embedding
+  dtype: float32
+  shape:
+  - batch
+  - 4096
+display:
+  summary: 'Light: classic ImageNet visual embeddings for fast first-pass grouping
+    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
+    - 4096
+    seed: 17
+  checks:
+    load_with: onnxruntime
+    execution_provider: CPUExecutionProvider
+    output_dtype: float32
+    finite: true
+    normalized_l2_range:
+    - 0.99
+    - 1.01
+provenance:
+  base_model: torchvision/alexnet-imagenet1k-v1
+  source_repository: https://pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html
+  original_model_name: AlexNet ImageNet
+  original_model_url: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
+  authors:
+  - Alex Krizhevsky
+  - Ilya Sutskever
+  - Geoffrey E. Hinton
+  paper_title: ImageNet Classification with Deep Convolutional Neural Networks
+  paper_url: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
+  upstream_license: BSD-3-Clause for TorchVision code; ImageNet-trained weights distributed
+    by PyTorch under their documented model terms.
+  training_data: ImageNet-1K supervised classification data.
+  citation: "@inproceedings{krizhevsky2012imagenet,\n  title={ImageNet Classification\
+    \ with Deep Convolutional Neural Networks},\n  author={Krizhevsky, Alex and Sutskever,\
+    \ Ilya and Hinton, Geoffrey E.},\n  booktitle={Advances in Neural Information\
+    \ Processing Systems},\n  year={2012}\n}\n"
+  conversion_note: EIDORA produced this ONNX conversion and is not the original model
+    creator.
+  export_date: '2026-07-10'
+  exporter_version: eidora-onnx-exporter 0.1.0
+model_card:
+  best_for:
+  - Fast baseline visual grouping.
+  - Small or exploratory image projects on ordinary laptops.
+  - Regression testing the EIDORA ONNX package pipeline.
+  not_ideal_for:
+  - Fine-grained visual similarity where modern self-supervised models perform better.
+  - Text, video, or audio inputs.
+  - Production-quality semantic image retrieval when a stronger model is acceptable.
+  limitations: AlexNet is an older supervised ImageNet model. It is useful as a lightweight
+    baseline, but modern CLIP, DINOv2, SigLIP, or domain-specific models will usually
+    produce stronger semantic similarity.
+  license:
+    id: bsd-3-clause
+    attribution: Converted to ONNX for EIDORA from the TorchVision AlexNet ImageNet
+      weights.
+  huggingface:
+    org: eidora
+    repo_name: alexnet_imagenet1k_4096
+    pipeline_tag: feature-extraction
+    tags:
+    - eidora
+    - eidora-model-zoo
+    - onnx
+    - onnxruntime
+    - embeddings
+    - image
+    - alexnet
+    - imagenet
+    - compute:light
+    - modality:image
+    datasets:
+    - imagenet-1k
+    metrics:
+    - cosine-similarity

model.onnx

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