Instructions to use cfia-ai-lab/rtdetr_v2_r50vd-101spp-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cfia-ai-lab/rtdetr_v2_r50vd-101spp-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="cfia-ai-lab/rtdetr_v2_r50vd-101spp-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("cfia-ai-lab/rtdetr_v2_r50vd-101spp-ft") model = AutoModelForObjectDetection.from_pretrained("cfia-ai-lab/rtdetr_v2_r50vd-101spp-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Model Card for rtdetr_v2_r50vd-101spp-ft
- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
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Model Card for rtdetr_v2_r50vd-101spp-ft
Model Details
Model Description
This is a RT-DETR V2 model fine tuned for object detection of 101 weed seed species related to the regulated REGAL species.
17908 train, 3166 test images, 7076 validation images.
Agrostemma githago
Agrostis canina
Allium vineale
Amaranthus albus
Amaranthus palmeri
Amaranthus powellii
Amaranthus retroflexus
Amaranthus tuberculatus
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
Brassica nigra
Bromus diandrus
Bromus hordeaceus
Bromus inermis
Bromus japonicus
Bromus riparius
Bromus secalinus
Buglossoides arvensis
Calystegia sepium
Carduus nutans
Carthamus tinctorius
Cenchrus longispinus
Centaurea calcitrapa
Centaurea diffusa
Centaurea melitensis
Centaurea solstitialis
Centaurea stoebe
Cirsium arvense
Cirsium vulgare
Conringia orientalis
Convolvulus arvensis
Cuscuta gronovii
Cuscuta campestris
Cuscuta europaea
Cyclachaena xanthiifolia
Cynoglossum officinale
Datura stramonium
Echium vulgare
Euphorbia cyparissias
Euphorbia esula
Fallopia convolvulus
Galeopsis tetrahit
Galium aparine
Galium spurium
Galega officinalis
Galega orientalis
Gypsophila vaccaria
Heracleum mantegazzianum
Heracleum sosnowskyi
Heracleum sphondylium
Iva axillaris
Jacobaea vulgaris
Lithospermum officinale
Lactuca sativa
Lactuca serriola
Linaria vulgaris
Lolium persicum
Lolium temulentum
Medicago lupulina
Medicago sativa
Neslia paniculata
Polygonum aviculare
Ranunculus acris
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
Sorghum bicolor
Sorghum halepense
Thlaspi arvense
Tripleurospermum inodorum
Tripleurospermum maritimum
Ulex europaeus
Vicia americana
Vicia cracca
Vicia pannonica
Vicia villosa
Viola arvensis
Viola odorata
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-101spp-ft
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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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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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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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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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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Model Card Authors [optional]
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Base model
PekingU/rtdetr_v2_r50vd


