ferrotorch/ssd300_vgg16

SSD300 with VGG-16 backbone, pretrained on COCO. Re-keyed from torchvision 0.21 ssd300_vgg16 (SSD300_VGG16_Weights.COCO_V1).

Provenance

• Upstream factory: torchvision.models.ssd300_vgg16 with weights="COCO_V1" (torchvision 0.21).
• Conversion script: ferrotorch/scripts/pin_pretrained_weights.py.
• Ferrotorch issue: https://github.com/dollspace/ferrotorch/issues/1130.
• Number of trainable parameters in upstream torchvision model: 35,641,826.
• SHA-256 of model.safetensors (this file is pinned in ferrotorch-hub/src/registry.rs): 2db78702af742ec5882bc62e068e5337f366bc1dc00f069c34bbce91c5109dfe.

How to load

use ferrotorch_vision::models::registry::get_model;
let model = get_model("ssd300_vgg16", /* pretrained = */ true, /* num_classes = */ 91).unwrap();

The loader downloads this file, verifies SHA-256, then calls Module::load_state_dict(state_dict, strict=false). strict=false is required because ferrotorch's Module::named_parameters() does not yet expose BatchNorm2d running statistics (running_mean / running_var), so those keys in this safetensors file are intentionally ignored at load time until ferrotorch issue #995 closes. They are still included here so re-uploading is unnecessary once that work lands.

Conversion notes

All upstream torchvision keys were mapped 1:1 to ferrotorch parameter slots.

Upstream license (verbatim, torchvision 0.21 LICENSE)

BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

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