English image_classifier_vit_base_patch16_224_in21k_ucSat ViTForImageClassification from YKXBCi

Description

Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_base_patch16_224_in21k_ucSat is a English model originally trained by YKXBCi.

Predicted Entities

buildings, denseresidential, storagetanks, tenniscourt, parkinglot, golfcourse, intersection, harbor, river, runway, mediumresidential, chaparral, freeway, overpass, mobilehomepark, baseballdiamond, agricultural, airplane, sparseresidential, forest, beach

Download Copy S3 URI

How to use


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

imageClassifier = ViTForImageClassification \
    .pretrained("image_classifier_vit_base_patch16_224_in21k_ucSat", "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_base_patch16_224_in21k_ucSat", "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.base_patch16_224_in21k_ucSat").predict("hen.JPEG")

Model Information

Model Name: image_classifier_vit_base_patch16_224_in21k_ucSat
Compatibility: Spark NLP 4.1.0+
License: Open Source
Edition: Official
Input Labels: [image_assembler]
Output Labels: [class]
Language: en
Size: 322.0 MB