English image_classifier_vit_autotrain_cifar10__base ViTForImageClassification from abhishek

Description

Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_autotrain_cifar10__base is a English model originally trained by abhishek.

Predicted Entities

deer, bird, dog, horse, automobile, truck, frog, ship, airplane, cat

Download Copy S3 URI

How to use


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

imageClassifier = ViTForImageClassification \
    .pretrained("image_classifier_vit_autotrain_cifar10__base", "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_autotrain_cifar10__base", "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.autotrain_cifar10__base").predict("hen.JPEG")

Model Information

Model Name: image_classifier_vit_autotrain_cifar10__base
Compatibility: Spark NLP 4.1.0+
License: Open Source
Edition: Official
Input Labels: [image_assembler]
Output Labels: [class]
Language: en
Size: 321.9 MB