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Food Depth Senet154

Trained 8 epochs on every 3. frame, batch 8, AMP. Encoder: timm legacysenet154 (stem pool0 included in Esenet). modelsenet.pth — final clean statedict (no module. prefix) checkpointlast.pth — resume checkpoint (model...

Sourcehuggingface.co/arunapb/food-depth-senet154/tree/070e22099127ff9c3fae7e4b4f69056a7344e2b1

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Model source

Kind: Other
File size: 601 MB–1.76 GB
Version: vCheckpoint last
Updated: Oct 10, 2026
Source: Hugging Face

Source description

SENet-154 monocular depth (Hu et al. 2018 architecture) — NYU-Depth V2

Trained 8 epochs on every 3. frame, batch 8, AMP. Encoder: timm legacy_senet154 (stem pool0 included in E_senet).

model_senet.pth — final clean state_dict (no module. prefix)
checkpoint_last.pth — resume checkpoint (model + optimizer + scaler + history)

Load with the matching architecture from the training notebook.

Sources

1 source

Hugging FacePrimaryVersion 070e22099127ff9c3fae7e4b4f69056a7344e2b1

huggingface.co/arunapb/food-depth-senet154/tree/070e22099127ff9c3fae7e4b4f69056a7344e2b1

Verified Oct 10

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Model artifacts

2 artifacts

checkpoint_last.pth

pth · 1.76 GB · SHA-256 759be68c3e10…fed6 · Hugging Face

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model_senet.pth

pth · 601 MB · SHA-256 2bbbf6ac6a9a…c78c · Hugging Face

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Source excerpts

2 excerpts

ReadmeAug 1huggingface.co/arunapb/food-depth-senet154

--- license: mit tags: [depth-estimation, nyu-depth-v2, senet154, pytorch] --- # SENet-154 monocular depth (Hu et al. 2018 architecture) — NYU-Depth V2 Trained 8 epochs on every 3. frame, batch 8, AMP. Encoder: timm `legacy_senet154` (stem pool0 included in E_senet). - `model_senet.pth` — final clean state_dict (no `module.` prefix) - `checkpoint_last.pth` — resume checkpoint (model + optimizer + scaler + history) Load with the matching architecture from the training notebook.

NoteAug 1huggingface.co/arunapb/food-depth-senet154

Source context: 0 downloads · 0 likes · Pipeline depth-estimation · Repo arunapb/food-depth-senet154