OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Damo Yolo

Damo Yolo is a Python object-detection project that provides model configurations, pretrained weights, inference tools, and training and fine-tuning workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Damo Yolo — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a code-based object-detection workflow and can work with the repository's Python and model-runtime setup, or when you want to explore its pretrained variants and fine-tuning materials.

Who it is for

  • Computer-vision developers running pretrained object detectors on image, video or camera input.
  • Practitioners adapting object-detection models to custom datasets.
  • Researchers and developers comparing Torch, ONNX and TensorRT model paths.

Documented uses

  • Run pretrained object detection on an image, video or camera feed.
  • Train a model using the documented COCO workflow.
  • Fine-tune a detector on custom data using the project's linked tutorial.
  • Choose among listed model variants and application-focused detection models.
Before you choose

Requirements & limits

The repository is public and displays an Apache-2.0 license. Its local quick-start specifies Python 3.7, PyTorch 1.7.0, torchvision 0.8.0, torchaudio 0.7.0, CUDA Toolkit 10.2 and pycocotools. The README separately describes selected ModelScope models as available for training with free GPU resources.

  • The inference examples require an engine file and configuration; the README lists Torch, ONNX and TensorRT formats.
  • The documented local setup specifies an older Python and PyTorch stack, which developers should account for when planning an environment.
  • The repository separates general model variants from application-focused detection models.
  • ModelScope is presented as a separate hosted training and demo route, rather than the same workflow as local installation.

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 the demo on an image, video or camera

Use a pretrained DAMO-YOLO engine with the repository's demo tool on one of its documented input types.

Before you start

  • A DAMO-YOLO installation following the repository's quick-start instructions.
  • A pretrained Torch, ONNX or TensorRT engine and its corresponding configuration file.
  • Image or video input, or a camera available to the runtime.

Steps

  1. Choose a pretrained engine and configuration from the repository's model materials.

  2. Select the demo input mode: image, video or camera.

  3. Provide the configuration with -f and the engine with --engine.

  4. Set the input with --path for an image or video, or --camid for a camera; the README examples also set confidence threshold, inference size and device.

Expected outcome

The documented demo workflow runs object detection against the selected image, video or camera input.

Guide limitations

  • The README examples use CUDA and do not establish support for every hardware or runtime configuration.
  • The guide describes the repository's documented workflow, not a separately verified execution.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
tinyvision/DAMO-YOLO · Official homepage title · retained in its source language
How we review sourcesSuggest a correction