Model Card for rtdetr_v2_r50vd-64spp-ft

Model Details

Model Description

This is a RT-DETR V2 model fine tuned for object detection of 64 weed seed species related to the regulated REGAL species.

• Agrostemma githago

• Agrostis canina

• Ambrosia artemisiifolia

• Ambrosia psilostachya

• Ambrosia trifida

• Anthoxanthum aristatum

• Anthoxanthum odoratum

• Apera spica-venti

• Asclepias syriaca

• Asclepias tuberosa

• Avena fatua

• Avena sativa

• Bassia scoparia

• Berteroa incana

• Brassica juncea

• Brassica napus

• Bromus hordeaceus

• Bromus inermis

• Bromus japonicus

• Bromus secalinus

• Buglossoides arvensis

• Calystegia sepium

• Carduus nutans

• Centaurea calcitrapa

• Centaurea diffusa

• Centaurea melitensis

• Centaurea solstitialis

• Centaurea stoebe

• Cirsium arvense

• Cirsium vulgare

• Conringia orientalis

• Convolvulus arvensis

• Cuscuta gronovii

• Cyclachaena xanthiifolia

• Fallopia convolvulus

• Galeopsis tetrahit

• Galium aparine

• Gypsophila vaccaria

• Iva axillaris

• Lithospermum officinale

• Lolium persicum

• Lolium temulentum

• Neslia paniculata

• Polygonum aviculare

• Saponaria officinalis

• Silene latifolia

• Silene noctiflora

• Silene vulgaris

• Sinapis alba

• Sinapis arvensis

• Solanum americanum

• Solanum carolinense

• Solanum elaeagnifolium

• Solanum emulans

• Solanum nigrum

• Solanum rostratum

• Sonchus arvensis

• Thlaspi arvense

• Tripleurospermum inodorum

• Tripleurospermum maritimum

• Vicia americana

• Vicia cracca

• Vicia villosa

• Viola arvensis

• Developed by: CFIA AI Lab and Seed Lab

• Funded by [optional]: [More Information Needed]

• Shared by [optional]: [More Information Needed]

• Model type: RT DETR V2 Transformer

• Language(s) (NLP): [More Information Needed]

• License: MIT

• Finetuned from model [optional]: PekingU/rtdetr_v2_r50vd

Model Sources [optional]

• Repository: cfia-ai-lab/rtdetr_v2_r50vd-64spp-ft
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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