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Detecto

Detecto is a Python package built on PyTorch for creating computer-vision and object-detection models, training on custom labeled datasets, running predictions on images and video, and saving model weights to files.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Detecto — 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 Detecto when a Python workflow needs object detection with PyTorch, especially when training on a custom labeled dataset or processing image and video inputs.

Who it is for

  • Python developers building object-detection workflows
  • Practitioners training detectors for custom object classes
  • Developers who need image or video inference and prediction visualization

Documented uses

  • Train a detector using images labeled for custom classes.
  • Run object detection on still images, videos or a live webcam.
  • Inspect predictions as labels, bounding boxes and scores.
  • Save trained model weights for later use.
Before you choose

Requirements & limits

The project documents installation with pip and specifies the MIT license.

  • Detecto is a Python library, so its documented workflows involve code and local data paths rather than a hosted interface.
  • The project builds on PyTorch and exposes its underlying torchvision model for additional control.
  • The README shows both a brief pretrained video-inference example and more configurable training workflows using transforms, validation data and model selection.

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 Detecto and run video inference

Install the package and apply a pretrained object-detection model to an input video.

Before you start

  • Python environment with pip
  • Input video file

Steps

  1. Install Detecto with the command below.

    Documented command
    pip3 install detecto

  2. Import Model from detecto.core and detect_video from detecto.visualize.

  3. Initialize model = Model() to load the documented pretrained model.

  4. Call detect_video(model, 'input_video.mp4', 'output.avi') with the input and output paths.

Expected outcome

The example runs object detection on the input video and writes an output video file.

Guide limitations

  • The example uses the pretrained Faster R-CNN ResNet-50 FPN model; custom classes require a training workflow.
  • The project says pip should install dependencies automatically, but lists requirements.txt as a manual dependency reference if an issue occurs.
Documentation 1 ↗
Source-based practical guide

Train a detector on a labeled custom dataset

Load labeled images, train a model for chosen object classes and save the trained weights.

Before you start

  • Detecto installed in a Python environment
  • Custom image dataset with image and label data
  • Object class names for the labels

Steps

  1. Load the dataset with Dataset('custom_dataset/').

  2. Initialize Model with the target class names, such as Model(['dog', 'cat', 'rabbit']).

  3. Train the model by calling model.fit(dataset).

  4. Save trained weights to a file with model.save('model_weights.pth').

Expected outcome

A trained object-detection model with weights saved to a file.

Guide limitations

  • The simple example assumes the custom dataset folder contains image and label data in a format Detecto can load.
  • The advanced example documents additional dataset, transform and training options, but this recipe does not configure them.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
alankbi/detecto · Official homepage title · retained in its source language
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