Description
Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_baseball_stadium_foods is a English model originally trained by nateraw.
Predicted Entities
nachos, popcorn, cotton candy, hot dog, hamburger
How to use
image_assembler = ImageAssembler() \
.setInputCol("image") \
.setOutputCol("image_assembler")
imageClassifier = ViTForImageClassification \
.pretrained("image_classifier_vit_baseball_stadium_foods", "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_baseball_stadium_foods", "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.baseball_stadium_foods").predict("hen.JPEG")
Model Information
| Model Name: | image_classifier_vit_baseball_stadium_foods |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [image_assembler] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 321.9 MB |