English image_classifier_vit_exper3_mesum5 ViTForImageClassification from sudo-s

Description

Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_exper3_mesum5 is a English model originally trained by sudo-s.

Predicted Entities

45, 98, 113, 34, 67, 120, 93, 142, 147, 12, 66, 89, 51, 124, 84, 8, 73, 78, 19, 100, 23, 62, 135, 128, 4, 121, 88, 77, 40, 110, 15, 11, 104, 90, 9, 141, 139, 132, 44, 33, 117, 22, 56, 55, 26, 134, 50, 123, 37, 68, 61, 107, 13, 46, 99, 24, 94, 83, 35, 16, 79, 5, 103, 112, 72, 10, 59, 144, 87, 48, 21, 116, 76, 138, 54, 43, 148, 127, 65, 71, 57, 108, 32, 80, 106, 137, 82, 49, 6, 126, 36, 1, 39, 140, 17, 25, 60, 14, 133, 47, 122, 111, 102, 31, 96, 69, 95, 58, 145, 64, 53, 42, 75, 115, 109, 0, 20, 27, 70, 2, 86, 38, 81, 118, 92, 125, 18, 101, 30, 7, 143, 97, 130, 114, 129, 29, 41, 105, 63, 3, 74, 91, 52, 85, 131, 28, 119, 136, 146

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How to use

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

imageClassifier = ViTForImageClassification \
    .pretrained("image_classifier_vit_exper3_mesum5", "en")\
    .setInputCols("image_assembler") \
    .setOutputCol("class")

pipeline = Pipeline(stages=[
    image_assembler,
    imageClassifier,
])

pipelineModel = pipeline.fit(imageDF)

pipelineDF = pipelineModel.transform(imageDF)

Model Information

Model Name:	image_classifier_vit_exper3_mesum5
Compatibility:	Spark NLP 4.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[image_assembler]
Output Labels:	[class]
Language:	en
Size:	322.3 MB

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