Description
Pretrained ViTForImageClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.giecom_vit_model_clasification_waste is a English model originally trained by Giecom.
How to use
image_assembler = ImageAssembler()\
.setInputCol("image")\
.setOutputCol("image_assembler")
imageClassifier = ViTForImageClassification.pretrained(""giecom_vit_model_clasification_waste","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("giecom_vit_model_clasification_waste","en")
.setInputCols("image_assembler")
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
val pipelineModel = pipeline.fit(imageDF)
val pipelineDF = pipelineModel.transform(imageDF)
Model Information
| Model Name: | giecom_vit_model_clasification_waste |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [image_assembler] |
| Output Labels: | [label] |
| Language: | en |
| Size: | 321.3 MB |
References
https://huggingface.co/Giecom/giecom-vit-model-clasification-waste