EIDORA schema-v3 validated ONNX release

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+validation/photo.png filter=lfs diff=lfs merge=lfs -text

README.md

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+# AlexNet Image Light
+
+AlexNet Image Light is a light image embedding model kept mainly as a reproducible baseline for comparing how older CNN features organize cultural image collections.
+
+## Source and provenance
+
+- Base model/checkpoint: `torchvision/alexnet-imagenet1k-v1`
+- Source: https://pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html
+- Original model: AlexNet
+- Architecture paper: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
+- Training data: ImageNet-1K
+
+## 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: `image`
+- Embedding dimensions: `4096`
+- Normalized: `true`
+- Similarity: `cosine`
+
+## Preparation
+
+```yaml
+width: 224
+height: 224
+layout: NCHW
+resize: short_edge_crop
+interpolation: bilinear
+rescale: 0.00392156862745098
+mean:
+- 0.485
+- 0.456
+- 0.406
+std:
+- 0.229
+- 0.224
+- 0.225
+alpha_background:
+- 0
+- 0
+- 0
+short_edge: 256
+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: 256
+    minimum: 224
+    maximum: 2048
+```
+
+## Intended use
+
+- Baseline comparisons against newer image embedding models.
+- Teaching, debugging, and validating the EIDORA ONNX conversion workflow.
+- Projects where compatibility and a simple architecture matter more than representation quality.
+
+## Limitations
+
+AlexNet is an older supervised ImageNet model. Its embedding geometry reflects ImageNet categories and may not align with culturally meaningful distinctions. Use it mainly as a baseline and compare it with ResNet, DINO, and CLIP on the target collection.
+
+## License and attribution
+
+Converted from TorchVision AlexNet weights; verify pretrained-weight redistribution terms before publishing the ONNX artifact.
+
+## Citation
+
+```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}
+}
+```

config.yaml

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+schema_version: 3
+id: eidora/image-alexnet-imagenet-light
+name: AlexNet Image Light
+version: 0.1.0
+modality: image
+license: other
+bibtex: "@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}"
+compatibility:
+  min_eidora_version: 0.1.0
+artifacts:
+- path: conversion_report.json
+  sha256: a7f417fc4d8969b4682b96fda6ad6863cebab4f89adb48d1998bfde6b926abbd
+  role: asset
+- path: model.onnx
+  sha256: deea3812842a09731d3a9ddc88dcace1049d1b39cafd25a902a39437b9b239a5
+  role: graph
+- path: validation.yaml
+  sha256: 18b12ddef178c1cf77334dde9e101c228c4b8c88ade6ed2f0eef3fc4d8f2bbb4
+  role: asset
+- path: validation/embedding.npy
+  sha256: 990d9316a16850630befdd0ec9cfe5584e6ac441302d87fb68a7c4adde2cfa33
+  role: asset
+- path: validation/photo.png
+  sha256: 1d70a03fc99c16e3aa674dbb9c7a8d0c127b45e1cf416118131451932b2f2389
+  role: asset
+- path: validation/pixels.npy
+  sha256: f559af24acc89a03d8d1b1b59037d301fcb4d77ab8bea2e0e64d355a226abc8b
+  role: asset
+- path: validation/random_image.npy
+  sha256: 2e7b24b32c98c0ae8c05f508b9b037c6c3e51b0ba35405e5f2594604c3acbf48
+  role: asset
+- path: validation/source_parity.json
+  sha256: 7b5f9e26cae0535eebff345800ffab3c5aaf2d9b4f228a6e4a71cf2e8bbf9756
+  role: asset
+runtime:
+  graph: model.onnx
+  inputs:
+  - name: pixel_values
+    source: pixels
+    dtype: float32
+    shape:
+    - batch
+    - 3
+    - 224
+    - 224
+  output:
+    name: embedding
+    dtype: float32
+    shape:
+    - batch
+    - 4096
+  execution_providers:
+  - CPUExecutionProvider
+preparation:
+  width: 224
+  height: 224
+  layout: NCHW
+  resize: short_edge_crop
+  interpolation: bilinear
+  rescale: 0.00392156862745098
+  mean:
+  - 0.485
+  - 0.456
+  - 0.406
+  std:
+  - 0.229
+  - 0.224
+  - 0.225
+  alpha_background:
+  - 0
+  - 0
+  - 0
+  short_edge: 256
+  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: 256
+      minimum: 224
+      maximum: 2048
+  resize_rounding: truncate
+embedding:
+  dimensions: 4096
+  normalized: true
+  similarity: cosine
+metadata:
+  summary: 'Light: classic CNN baseline for fast comparison and first-pass embedding
+    experiments.'
+  trained_on: ImageNet-1K
+  feature_type: embedding
+  pooling: second fully-connected layer after ReLU
+  group:
+    id: image
+    label: Image Models
+    order: 10
+  provenance:
+    base_model: torchvision/alexnet-imagenet1k-v1
+    source_repository: https://pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html
+    original_name: AlexNet
+    original_model_url: https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html
+    paper_title: ImageNet Classification with Deep Convolutional Neural Networks
+    paper_url: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
+    training_data: ImageNet-1K
+    upstream_license: TorchVision software is BSD-3-Clause. TorchVision states that
+      pretrained models may have their own terms derived from training data; weight
+      redistribution must be reviewed before public release.
+    authors:
+    - Alex Krizhevsky
+    - Ilya Sutskever
+    - Geoffrey E. Hinton
+    source_weights: IMAGENET1K_V1
+    source_weights_url: https://download.pytorch.org/models/alexnet-owt-7be5be79.pth
+    exported_opset: 17
+    export_date: '2026-10-21'
+    exporter_version: eidora-onnx 1.0.0

conversion_report.json

@@ -0,0 +1,18 @@
+{
+  "converter": "torchvision_alexnet",
+  "embedding_dimensions": 4096,
+  "input_name": "pixel_values",
+  "opset": 17,
+  "output_name": "embedding",
+  "schema_version": 1,
+  "source_framework": "pytorch",
+  "source_vs_onnx": {
+    "atol": 0.0001,
+    "max_abs_error": 1.043081283569336e-07,
+    "mean_abs_error": 1.883620370790595e-09,
+    "onnxruntime_version": "1.30.0",
+    "rtol": 0.001,
+    "status": "passed"
+  },
+  "torch_version": "2.14.0+cu130"
+}

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:deea3812842a09731d3a9ddc88dcace1049d1b39cafd25a902a39437b9b239a5
+size 228019732

validation-report.json

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+{
+  "desktop_version": "0.2.0",
+  "schema_version": 3,
+  "preparation_version": 2,
+  "providers": {
+    "CPUExecutionProvider": {
+      "status": "passed",
+      "fixture_runs": 19,
+      "scope": "session provider chain; CPU graph-node support allowed",
+      "environment": {
+        "onnxruntime": "1.31.0",
+        "platform": "Linux-7.0.0-38-generic-x86_64-with-glibc2.43",
+        "python": "3.13.16 (main, Oct  9 2026, 15:23:36) [Clang 22.1.3 ]"
+      }
+    }
+  },
+  "environment": {
+    "onnxruntime": "1.31.0",
+    "platform": "Linux-7.0.0-38-generic-x86_64-with-glibc2.43",
+    "python": "3.13.16 (main, Oct  9 2026, 15:23:36) [Clang 22.1.3 ]"
+  },
+  "publication_ready": true,
+  "validation_sha256": "df0c747d8cd0734a8dcad20da6afe6f74d437b51e431845f633577e714074167",
+  "contract_sha256": "65c20b19452a5cedc6874434d8379a280d6c9b4343a4deab6d9772d93ee8d6b0",
+  "package_sha256": "5dbe16d8f71ed8ce5c6507a74faeed81a02b3de3d98e1fe588c54ed4ad839093"
+}

validation.yaml

@@ -0,0 +1,21 @@
+diagnostic_fixtures:
+- id: random_image
+  path: validation/random_image.npy
+  input_shape:
+  - 1
+  - 3
+  - 224
+  - 224
+  expected_shape:
+  - 1
+  - 4096
+  seed: 17
+fixtures:
+- modality: image
+  path: validation/photo.png
+  expected_tensors:
+    pixel_values: validation/pixels.npy
+  expected_vectors: validation/embedding.npy
+note: Publication requires real-media fixtures with source-reference expected_tensors
+  and expected_vectors, copied using assets. Synthetic diagnostic fixtures do not
+  certify compatibility.

validation/embedding.npy

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+oid sha256:990d9316a16850630befdd0ec9cfe5584e6ac441302d87fb68a7c4adde2cfa33
+size 16512

validation/pixels.npy

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+oid sha256:f559af24acc89a03d8d1b1b59037d301fcb4d77ab8bea2e0e64d355a226abc8b
+size 602240

validation/random_image.npy

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+oid sha256:2e7b24b32c98c0ae8c05f508b9b037c6c3e51b0ba35405e5f2594604c3acbf48
+size 602240

validation/source_parity.json

@@ -0,0 +1,15 @@
+{
+  "model": "alexnet",
+  "source_checkpoint": "IMAGENET1K_V1",
+  "image_sha256": "1d70a03fc99c16e3aa674dbb9c7a8d0c127b45e1cf416118131451932b2f2389",
+  "onnx_sha256": "deea3812842a09731d3a9ddc88dcace1049d1b39cafd25a902a39437b9b239a5",
+  "preprocessing_max_abs_error": 7.152557373046875e-07,
+  "embedding_max_abs_error": 1.7695128917694092e-07,
+  "cosine_similarity": 1.0000001192092896,
+  "output_shape": [
+    1,
+    4096
+  ],
+  "source": "TorchVision pretrained model",
+  "status": "passed"
+}