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Captioning Inceptionv3 Transformer

InceptionV3 + Transformer image captioning architecture.

TypeModèleVersionRevision 59d93b4CClasse

Source du modèle

Type
Modèle
Taille du fichier
216 MB
Version
vRevision 59d93b4
Mis à jour
30 oct. 2026
Téléchargements
0
Source
Hugging Face

Description de la source

Image Captioning System — Dev Scaffold (v1.0.0)

InceptionV3 + Transformer image captioning architecture.

This release contains a deployment scaffold used for end-to-end system validation and infrastructure testing. It is intentionally published before the production training run so the full serving stack (FastAPI backend, Hugging Face Spaces container, Vercel frontend, GitHub Actions CI/CD) can be exercised end-to-end.

Purpose

  • FastAPI inference serving
  • Hugging Face Hub snapshot_download integration
  • Frontend / backend deployment validation
  • CI/CD pipeline validation
  • Production ML system architecture demonstration

Architecture

  • Encoder: frozen InceptionV3 (ImageNet weights, 2048-dim features)
  • Decoder: single Transformer decoder layer, d_model=512, 8 heads
  • Vocab size: 52 tokens (scaffold) — production target is 15,000 (COCO)
  • Max caption length: 40 tokens

⚠️ Current limitations

The decoder weights are bootstrap development artefacts generated by a synthetic 10-sentence corpus, not trained on the full COCO dataset. Caption outputs will be incoherent and limited to the 52-token scaffold vocabulary. The encoder is fully functional (real ImageNet weights); only the decoder is untrained.

Future revisions will replace these weights with a model trained on MS COCO 2017 via scripts/train.py and configs/train/stabilized.yaml.

Files

FileSizeSHA-256
model.h5158 MBbfe020d920aa2f3d019bf7b5b33904384057372e7c304a9e101a2a59fe110084
vocab.json566 B45ec1704d73046303cbd5292590b2e204b194a2d8345dfb84de81370b4ab4eef
vocab.pkl3,013 Bc6700d2bbcd8dc705d6b0ca53e0f8848baa6225e9b3e836036d94ab5accd306c

Usage

This repo is consumed by the backend via huggingface_hub.snapshot_download:

BACKEND_WEIGHTS_HUB_REPO=apoorvrajdev/captioning-inceptionv3-transformer
BACKEND_WEIGHTS_HUB_REVISION=v1.0.0
BACKEND_WEIGHTS_HUB_FILENAME=model.h5

Sources

1 source
Hugging FacePrincipalVersion 59d93b4babb16b0ac81eef598f3abc271a355cbf
huggingface.co/apoorvrajdev/captioning-inceptionv3-transformer/tree/59d93b4babb16b0ac81eef598f3abc271a355cbf

Vérifié 30 oct.

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Repository files

File inventory only; completeness is unverified.

Artefacts du modèle

1 artefact

model.h5

h5 · 216 MB · SHA-256 74963a3f7cd0…9b07 · Hugging Face

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Extraits de sources

2 extraits

--- license: mit library_name: keras tags: - image-captioning - tensorflow - keras - transformer - inceptionv3 - multimodal - dev-scaffold pipeline_tag: image-to-text --- # Image Captioning System — Dev Scaffold (v1.0.0) InceptionV3 + Transformer image captioning architecture. This release contains a **deployment scaffold** used for end-to-end system validation and infrastructure testing. It is intentionally published before the production training run so the full serving stack (FastAPI backend, Hugging Face Spaces container, Vercel frontend, GitHub Actions CI/CD) can be exercised end-to-end. ## Purpose - FastAPI inference serving - Hugging Face Hub `snapshot_download` integration - Frontend / backend deployment validation - CI/CD pipeline validation - Production ML system architecture demonstration ## Architecture - **Encoder:** frozen InceptionV3 (ImageNet weights, 2048-dim features) - **Decoder:** single Transformer decoder layer, d_model=512, 8 heads - **Vocab size:** 52 tokens (scaffold) — production target is 15,000 (COCO) - **Max caption length:** 40 tokens ## ⚠️ Current limitations The decoder weights are **bootstrap development artefacts** generated by a synthetic 10-sentence corpus, not trained on the full COCO dataset. Caption outputs will be incoherent and limited to the 52-token scaffold vocabulary. The encoder is fully functional (real ImageNet weights); only the decoder is untrained. Future revisions will replace these weights with a model trained on MS COCO 2017 via `scripts/train.py` and `configs/train/stabilized.yaml`. ## Files | File | Size | SHA-256 | |---|---:|---| | `model.h5` | 158 MB | `bfe020d920aa2f3d019bf7b5b33904384057372e7c304a9e101a2a59fe110084` | | `vocab.json` | 566 B | `45ec1704d73046303cbd5292590b2e204b194a2d8345dfb84de81370b4ab4eef` | | `vocab.pkl` | 3,013 B | `c6700d2bbcd8dc705d6b0ca53e0f8848baa6225e9b3e836036d94ab5accd306c` | ## Usage This repo is consumed by the backend via `huggingface_hub.snapshot_download`: ```env BACKEND_WEIGHTS_HUB_REPO=apoorvrajdev/captioning-inceptionv3-transformer BACKEND_WEIGHTS_HUB_REVISION=v1.0.0 BACKEND_WEIGHTS_HUB_FILENAME=model.h5

Source context: 0 downloads · 0 likes · Pipeline image-to-text · Library keras · Repo apoorvrajdev/captioning-inceptionv3-transformer