Swin Large Patch4 Window12 384 In22k 101spp Ft Dff

Model Card for swin-large-patch4-window12-384-in22k-101spp-ft

Model Details

Model Description

This is a SWIN model fine tuned to classify 101 weed seed species related to the regulated REGAL species.
73841 train, 13031 test, 26814 validation

• 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

• Xanthium strumarium
  Samples Training Loss Epoch metrics Overall Top K Accuracy Class Metrics Confusion Matrix

• Developed by: CFIA AI Lab and Seed Lab

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

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

• Model type: SWIN Transformer

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

• License: MIT

• Finetuned from model [optional]: microsoft/swin-large-patch4-window12-384-in22k

Model Sources [optional]

• Repository: cfia-ai-lab/swin-large-patch4-window12-384-in22k-101spp-ft
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

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

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]