Description
Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_dog_breed_classifier is a English model originally trained by skyau.
Predicted Entities
whippet, cairn, Old_English_sheepdog, Rottweiler, American_Staffordshire_terrier, Blenheim_spaniel, Leonberg, bluetick, Yorkshire_terrier, African_hunting_dog, Doberman, Appenzeller, Boston_bull, German_shepherd, kuvasz, standard_poodle, Chesapeake_Bay_retriever, toy_terrier, Australian_terrier, Dandie_Dinmont, Brittany_spaniel, basenji, Newfoundland, Airedale, giant_schnauzer, Bouvier_des_Flandres, golden_retriever, Welsh_springer_spaniel, Pekinese, West_Highland_white_terrier, briard, Gordon_setter, Border_collie, Pomeranian, Scotch_terrier, malamute, EntleBucher, toy_poodle, Mexican_hairless, clumber, Scottish_deerhound, curly-coated_retriever, Bedlington_terrier, soft-coated_wheaten_terrier, Irish_setter, Lhasa, bloodhound, French_bulldog, standard_schnauzer, Chihuahua, borzoi, Sealyham_terrier, malinois, Norwegian_elkhound, Staffordshire_bullterrier, bull_mastiff, Ibizan_hound, komondor, Kerry_blue_terrier, Saint_Bernard, basset, Eskimo_dog, Sussex_spaniel, English_springer, flat-coated_retriever, cocker_spaniel, Tibetan_terrier, Shih-Tzu, beagle, silky_terrier, Saluki, vizsla, pug, Shetland_sheepdog, Maltese_dog, Norwich_terrier, kelpie, Italian_greyhound, Walker_hound, Greater_Swiss_Mountain_dog, miniature_schnauzer, Great_Pyrenees, Tibetan_mastiff, collie, Siberian_husky, Bernese_mountain_dog, Irish_wolfhound, chow, boxer, Great_Dane, dingo, Japanese_spaniel, Rhodesian_ridgeback, Border_terrier, Afghan_hound, Irish_water_spaniel, black-and-tan_coonhound, redbone, Norfolk_terrier, affenpinscher, Brabancon_griffon, miniature_pinscher, Labrador_retriever, Lakeland_terrier, groenendael, schipperke, papillon, wire-haired_fox_terrier, Cardigan, English_foxhound, Pembroke, dhole, German_short-haired_pointer, miniature_poodle, Irish_terrier, Weimaraner, otterhound, English_setter, Samoyed, keeshond
Live Demo
Open in Colab
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How to use
Scala NLU
image_assembler = ImageAssembler () \
. setInputCol ( "image" ) \
. setOutputCol ( "image_assembler" )
imageClassifier = ViTForImageClassification \
. pretrained ( "image_classifier_vit_dog_breed_classifier" , "en" ) \
. setInputCols ( "image_assembler" ) \
. setOutputCol ( "class" )
pipeline = Pipeline ( stages = [
image_assembler ,
imageClassifier ,
])
pipelineModel = pipeline . fit ( imageDF )
pipelineDF = pipelineModel . transform ( imageDF )
val imageAssembler = new ImageAssembler ()
. setInputCol ( "image" )
. setOutputCol ( "image_assembler" )
val imageClassifier = ViTForImageClassification
. pretrained ( "image_classifier_vit_dog_breed_classifier" , "en" )
. setInputCols ( "image_assembler" )
. setOutputCol ( "class" )
val pipeline = new Pipeline (). setStages ( Array ( imageAssembler , imageClassifier ))
val pipelineModel = pipeline . fit ( imageDF )
val pipelineDF = pipelineModel . transform ( imageDF )
import nlu
import requests
response = requests . get ( 'https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp/master/docs/assets/images/hen.JPEG' )
with open ( 'hen.JPEG' , 'wb' ) as f :
f . write ( response . content )
nlu . load ( "en.classify_image.dog_breed_classifier" ). predict ( "hen.JPEG" )
|—|—|
Model Name:
image_classifier_vit_dog_breed_classifier
Compatibility:
Spark NLP 4.1.0+
Input Labels:
[image_assembler]