DINO

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DINO is an open-source object detection model that implements "DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection." It achieves state-of-the-art performance with fast convergence.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is DINO?

DINO (DETR with Improved DeNoising Anchor Boxes) is an open-source object detection model, officially implemented from a paper accepted at ICLR 2023. It offers state-of-the-art, end-to-end object detection capabilities, achieving high accuracy on benchmarks like COCO Val and COCO test-dev with significantly smaller model and data sizes compared to previous best models. A key differentiator is its fast convergence, with the ResNet-50 backbone achieving 49.4 AP in just 12 epochs. The project provides model checkpoints, training logs, and detailed installation instructions, making it accessible for researchers and developers. It also supports various backbones like Swin-L and offers scripts for evaluation, inference, visualization, and distributed training.

Best used for

Ideal for developers and data scientists who need to implement advanced object detection models, conduct research in computer vision, and fine-tune models on custom datasets. Especially valuable for those seeking state-of-the-art performance and fast convergence in their detection tasks.

Common actions

implement object detection

train computer vision models

evaluate detection performance

low-code/no-codedeepfakeworkflowsautomated workflowcollaborationopen-source"AI Agents"github copilotface swapping

Capabilities

Key features

End-to-end object detection
Improved denoising anchor boxes
Fast-converging models
State-of-the-art AP
Supports multiple backbones
Distributed training support

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary innovation of DINO?

DINO introduces "DETR with Improved DeNoising Anchor Boxes," which significantly enhances end-to-end object detection. This innovation leads to state-of-the-art performance on benchmarks like COCO while maintaining smaller model and data sizes and achieving faster convergence.

What are the performance metrics of DINO?

DINO achieves 63.2 AP on COCO Val and 63.3 AP on COCO test-dev. With a ResNet-50 backbone, the 5-scale model reaches 49.4 AP in 12 epochs and 51.3 AP in 24 epochs, demonstrating its fast convergence and high accuracy.

Can DINO be trained on custom datasets?

Yes, DINO can be trained on custom datasets. Users need to tune parameters like `num_classes` and `dn_labebook_size` in the configuration file. It also supports fine-tuning from pre-trained models by specifying the model path and ignoring inconsistent parameters.

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