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Clip Caption Reward

Clip Caption Reward is a Python and PyTorch research project for fine-grained image captioning using a CLIP-based reward. It provides code for preprocessing, model training and evaluation, links to pretrained checkpoints, and references the FineCapEval evaluation dataset.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Clip Caption Reward — 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 need research code and datasets/checkpoint references for experimenting with fine-grained image captions and reward-based training, rather than a ready-made hosted captioning service.

Who it is for

  • Researchers reproducing or adapting fine-grained image-captioning experiments.
  • Practitioners comparing captioning models with CLIP-based or text-metric rewards.

Documented uses

  • Preprocess COCO or FineCapEval images into features for captioning experiments.
  • Train a captioning model with maximum-likelihood estimation or fine-tune it with reinforcement learning.
  • Evaluate generated captions on FineCapEval or use the documented CLIP retrieval evaluation.
Before you choose

Requirements & limits

  • The README suggests Python 3.7 and installation of the listed requirements and repository package.
  • Captioning and CLIP checkpoints are linked through Hugging Face; the README lists checkpoint files of about 669.65 MB and 1.12 GB.
  • COCO and FineCapEval image and annotation files need to be obtained and placed in the documented dataset structure.
  • Detectron2, COCO-caption tools, and Java for METEOR are listed as optional dependencies for specific utilities or evaluation metrics.

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

Install the CLIP-Caption-Reward Python package

Set up the repository and install its Python dependencies.

Before you start

  • A Python environment; the README gives Python 3.7 in an optional Conda environment example.
  • A local checkout of the repository.

Steps

  1. Optionally create and activate the example environment: conda create -n clip4caption python=3.7 followed by source activate clip4caption.

  2. From the repository, install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  3. Install the repository as a package with the command below.

    Documented command
    pip install -e .

Expected outcome

The repository and its listed Python dependencies are installed in the active environment.

Guide limitations

  • This covers setup only; it does not run inference, training, or evaluation.
  • Detectron2, COCO caption evaluation tools, and Java for METEOR are described as optional and are not included here.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
j-min/CLIP-Caption-Reward · Official homepage title · retained in its source language
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