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Pytorch Image Captioning

Pytorch Image Captioning is a Python and PyTorch implementation that generates natural-language descriptions of images using a pretrained ResNet50 encoder and a six-block Transformer decoder.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pytorch Image Captioning — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a documented PyTorch image-captioning implementation to study or adapt, or when you want to follow its Flickr8K training workflow.

Who it is for

  • Developers prototyping image-to-text features for computer-vision applications.
  • Learners exploring CNN and Transformer approaches to image captioning.

Documented uses

  • Generate a caption from an image.
  • Study how image feature maps and previously generated words are combined in a Transformer decoder.
  • Prepare Flickr8K data and start training the repository's model.
Before you choose

Requirements & limits

  • The repository is Python-based and uses PyTorch.
  • The documented setup requires Flickr8K and GloVe glove.6B.zip.
  • The README reports BLEU evaluation with greedy decoding and says beam search is not implemented.
  • The repository is licensed under MIT.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the Flickr8K training environment

Set up the repository environment and prepare the dataset and GloVe embeddings for training.

Before you start

  • Flickr8K dataset
  • GloVe embeddings archive named glove.6B.zip
  • Anaconda Prompt and the repository's environment.yml

Steps

  1. Acquire Flickr8K using the dataset link or instructions referenced in the README.

  2. Download glove.6B.zip and create the Conda environment with conda env create -f environment.yml.

  3. Activate it with conda activate pytorch-image-captioning, then set the glove_dir entry in config.json to the GloVe directory.

  4. Run python prepare_dataset.py to preprocess captions, build the vocabulary and embeddings, and separate mappings by the predefined split.

Expected outcome

The documented preparation process creates processed caption data, vocabulary and GloVe embeddings for the predefined dataset split.

Guide limitations

  • The dataset and GloVe files must be acquired separately.
  • The instructions describe preparation; training is a separate next step.
Documentation 1 ↗
Source-based practical guide

Start model training

Initiate training after preparing the dataset and environment.

Before you start

  • A prepared dataset from python prepare_dataset.py
  • The activated pytorch-image-captioning Conda environment

Steps

  1. Navigate to the repository directory in Anaconda Prompt.

  2. Activate the environment with conda activate pytorch-image-captioning.

  3. Run python main.py to initiate model training.

Expected outcome

The repository's training process is initiated.

Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
senadkurtisi/pytorch-image-captioning · Official homepage title · retained in its source language
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