English image_classifier_vit_mit_indoor_scenes ViTForImageClassification from vincentclaes

Description

Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_mit_indoor_scenes is a English model originally trained by vincentclaes.

Predicted Entities

airport_inside, bowling, buffet, movietheater, clothingstore, inside_bus, fastfood_restaurant, operating_room, corridor, cloister, stairscase, auditorium, meeting_room, livingroom, videostore, bathroom, inside_subway, bedroom, casino, tv_studio, classroom, laboratorywet, nursery, office, deli, prisoncell, dentaloffice, restaurant_kitchen, studiomusic, locker_room, restaurant, laundromat, dining_room, subway, gameroom, museum, mall, garage, elevator, jewelleryshop, kindergarden, toystore, concert_hall, artstudio, kitchen, florist, waitingroom, grocerystore, library, bar, computerroom, trainstation, lobby, church_inside, pantry, closet, children_room, hairsalon, shoeshop, greenhouse, bookstore, bakery, poolinside, warehouse, winecellar, hospitalroom, gym

Live Demo Open in Colab Download Copy S3 URI

How to use

image_assembler = ImageAssembler() \
    .setInputCol("image") \
    .setOutputCol("image_assembler")

imageClassifier = ViTForImageClassification \
    .pretrained("image_classifier_vit_mit_indoor_scenes", "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_mit_indoor_scenes", "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.mit_indoor_scenes").predict("hen.JPEG")