README.md

metadata

title: Image Caption Generator
emoji: 👁
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 6.26.0
python_version: '3.12'
app_file: app.py
pinned: false

Image Caption Generator

Upload an image and get an automatically generated caption. Built with a ResNet50 (frozen, transfer learning) + LSTM decoder, trained on the Flickr8k dataset.

This Space runs the regularized model checkpoint with beam search decoding (beam width 3) — the best-performing configuration from the project's optimization experiments (BLEU-4 = 0.1557 on the held-out test set, a ~27% relative improvement over the initial baseline).

• Full project, code, and documentation: https://github.com/adhamashraf7788/Image-Caption-Generator
• Model weights: https://huggingface.co/AdhamAshraf/image-caption-generator

How it works

1. Image is resized/cropped/normalized and passed through a frozen, ImageNet-pretrained ResNet50 to extract a 2048-d feature vector.
2. The feature vector is projected into a 256-d embedding and fed as the first input to an LSTM decoder.
3. The LSTM generates the caption word-by-word using beam search (keeping the top-3 candidate sequences at each step, rather than greedily committing to a single best word).

See the main repo's README for full details on the dataset, architecture, training process, evaluation metrics, and optimization experiments (beam search + regularization comparisons).

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference