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Trolo

Trolo is an Apache-2.0-licensed SDK and framework for applying transformer-based models, YOLO and other single-shot detectors to computer-vision tasks. It provides CLI and Python interfaces for inference, training and ONNX model export.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Trolo — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Trolo when you need a CLI or Python workflow for detector inference, model training or ONNX export, particularly when working with transformer-based models, YOLO or other single-shot detectors.

Who it is for

  • Developers building object-detection workflows with transformer-based or single-shot detector models.
  • Computer-vision practitioners who want to run inference on images, folders, video or webcam input.
  • Users exploring detector models through the linked Colab quickstart notebook.

Documented uses

  • Run detection inference on an image, image folder, video or webcam stream.
  • Retrieve predictions and visualize or save inference outputs through the Python API.
  • Train a detection model using a built-in configuration or custom configuration path.
  • Export a detector model in ONNX format.
Before you choose

Requirements & limits

The public repository identifies the project as open source under the Apache-2.0 license. The README documents installation with pip and includes Docker usage examples.

  • The README specifically documents D-FINE model options and lists RT-DETR v2 and v3 as coming soon, with Trolo-2024 marked as work in progress.
  • Inference supports single images, image folders, video files and webcam input; streaming video inference is also described.
  • The documented training CLI can accept GPU device IDs for automatic distributed data-parallel handling.
  • The Docker notes say GPU use with --gpus all requires NVIDIA Container Toolkit.
  • The README describes the package as an early release under active development.

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

Run detector inference on an input

Use Trolo's documented CLI to run a named or local detector model on an image, folder, video or webcam input.

Before you start

  • Install Trolo with pip.
  • Choose a supported model, such as dfine-n, or provide a local model file.
  • Provide a single image, image folder, video file or webcam input.

Steps

  1. Install the package with the command below.

    Documented command
    pip install trolo

  2. For a named model, run trolo predict --model dfine-n.

  3. For a specified input, run trolo predict --model dfine-n.pth --input img.jpg, substituting a folder, video file or 0 for webcam input as appropriate.

Expected outcome

Trolo runs inference on the selected input; the Python API also documents prediction and visualization or saving of outputs.

Guide limitations

  • The README labels Trolo an early release under active development.
  • The guide does not prescribe hardware or environment setup beyond package installation.
Documentation 1 ↗
Source-based practical guide

Export a detector model to ONNX

Use Trolo's documented CLI example to export a detector model in ONNX format.

Before you start

  • Install Trolo with pip.
  • Select a model and input size; the example uses dfine-n and size 640.

Steps

  1. Install the package with the command below.

    Documented command
    pip install trolo

  2. Run trolo export --model dfine-n --export_format onnx --input_size 640.

Expected outcome

Trolo's export command is configured to produce an ONNX-format model.

Guide limitations

  • The README refers readers to separate export documentation for more details.
  • The documented example does not specify further export dependencies or target-runtime requirements.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
TroloRun detection inference on an image, image folder, video or webcam stream.CloudThe public repository identifies the project as open source under the Apache-2.0 license. The README documents installation with pip and includes Docker usage examples.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
AyushExel/trolo · Official homepage title · retained in its source language
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