schema_version: 1
id: EIDORA/ResNet50_IN1k
name: ResNet50_IN1k
version: 1.0.0
model_family: ResNet
backend: onnx
file_size: 94 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: 09c59c1eba07408c5174b5fd58774bd16495cf0503f0406b0accfb2502ae6df0
  package_size_bytes: 93951417
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: 256
      crop_width: 224
      crop_height: 224
      interpolation: bilinear
      source: TorchVision ResNet50_Weights.IMAGENET1K_V2.transforms
    rescale: 1/255
    normalize:
      mean:
      - 0.485
      - 0.456
      - 0.406
      std:
      - 0.229
      - 0.224
      - 0.225
      inside_onnx: true
embedding:
  dimensions: 2048
  feature_type: embedding
  pooling: feature_layer
  normalized: true
  similarity: cosine
  output_name: embedding
  dtype: float32
  shape:
  - batch
  - 2048
display:
  summary: 'Medium: reliable ImageNet visual embeddings with moderate CPU runtime.'
  compute_tier: medium
  modality_labels:
  - image
  recommended_batch_size: 8
validation:
  fixtures:
  - id: image_tensor_001
    input_shape:
    - 1
    - 3
    - 224
    - 224
    expected_shape:
    - 1
    - 2048
    seed: 51
  checks:
    load_with: onnxruntime
    execution_provider: CPUExecutionProvider
    output_dtype: float32
    finite: true
    normalized_l2_range:
    - 0.99
    - 1.01
provenance:
  base_model: torchvision/resnet50-imagenet1k-v2
  source_repository: https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html
  original_model_name: ResNet-50 ImageNet
  original_model_url: https://github.com/pytorch/vision
  authors:
  - Kaiming He
  - Xiangyu Zhang
  - Shaoqing Ren
  - Jian Sun
  paper_title: Deep Residual Learning for Image Recognition
  paper_url: https://arxiv.org/abs/1512.03385
  upstream_license: BSD-3-Clause for TorchVision code; ImageNet weights are distributed
    by PyTorch under documented model terms.
  training_data: ImageNet-1K supervised classification data.
  citation: "@inproceedings{he2016deep,\n  title={Deep Residual Learning for Image\
    \ Recognition},\n  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and\
    \ Sun, Jian},\n  booktitle={CVPR},\n  year={2016}\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:
  - General image grouping and retrieval.
  - Projects that need a well-established visual baseline.
  not_ideal_for:
  - Text, video, or audio inputs.
  - Fine-grained semantic retrieval where self-supervised models are preferred.
  limitations: ResNet-50 is trained for ImageNet classification, so its embeddings
    may underperform specialized or self-supervised models on some visual domains.
  license:
    id: bsd-3-clause
    attribution: Converted to ONNX for EIDORA from TorchVision ResNet-50 ImageNet
      weights.
  huggingface:
    org: eidora
    repo_name: RESNET50_2048
    pipeline_tag: feature-extraction
    tags:
    - eidora
    - eidora-model-zoo
    - onnx
    - onnxruntime
    - embeddings
    - image
    - resnet
    - compute:medium
    - modality:image
    datasets:
    - imagenet-1k
    metrics:
    - cosine-similarity