cfia-ai-lab/rtdetr_v2_r50vd-64spp-ft
Model Card for rtdetrv2r50vd-64spp-ft
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Model Details
Model Description
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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
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- Model type: RT DETR V2 Transformer
- Language(s) (NLP): [More Information Needed]
- License: MIT
- Finetuned from model [optional]: PekingU/rtdetrv2r50vd
Model Sources [optional]
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- Repository: cfia-ai-lab/rtdetrv2r50vd-64spp-ft
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Uses
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Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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Training Details
Training Data
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Preprocessing [optional]
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Training Hyperparameters
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Environmental Impact
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
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