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+---
+layout: model
+title: Image Zero Shot Classification with CLIP
+author: John Snow Labs
+name: zero_shot_classifier_clip_vit_base_patch32
+date: 2023-12-02
+tags: [classification, image, en, zero_shot, open_source, onnx]
+task: Zero-Shot Classification
+language: en
+edition: Spark NLP 5.2.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CLIPForZeroShotClassification
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+CLIP (Contrastive Language-Image Pre-Training) is a neural network that was trained on image
+and text pairs. It has the ability to predict images without training on any hard-coded
+labels. This makes it very flexible, as labels can be provided during inference. This is
+similar to the zero-shot capabilities of the GPT-2 and 3 models.
+
+This model was imported from huggingface transformers:
+https://huggingface.co/openai/clip-vit-base-patch32
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+
+
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/zero_shot_classifier_clip_vit_base_patch32_en_5.2.0_3.0_1701541274927.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/zero_shot_classifier_clip_vit_base_patch32_en_5.2.0_3.0_1701541274927.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+import sparknlp
+from sparknlp.base import *
+from sparknlp.annotator import *
+from pyspark.ml import Pipeline
+
+imageDF = spark.read \
+ .format("image") \
+ .option("dropInvalid", value = True) \
+ .load("src/test/resources/image/")
+
+imageAssembler: ImageAssembler = ImageAssembler() \
+ .setInputCol("image") \
+ .setOutputCol("image_assembler")
+
+candidateLabels = [
+ "a photo of a bird",
+ "a photo of a cat",
+ "a photo of a dog",
+ "a photo of a hen",
+ "a photo of a hippo",
+ "a photo of a room",
+ "a photo of a tractor",
+ "a photo of an ostrich",
+ "a photo of an ox"]
+
+imageClassifier = CLIPForZeroShotClassification \
+ .pretrained() \
+ .setInputCols(["image_assembler"]) \
+ .setOutputCol("label") \
+ .setCandidateLabels(candidateLabels)
+
+pipeline = Pipeline().setStages([imageAssembler, imageClassifier])
+pipelineDF = pipeline.fit(imageDF).transform(imageDF)
+pipelineDF \
+ .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "label.result") \
+ .show(truncate=False)
+```
+```scala
+import com.johnsnowlabs.nlp.ImageAssembler
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+val imageDF = ResourceHelper.spark.read
+ .format("image")
+ .option("dropInvalid", value = true)
+ .load("src/test/resources/image/")
+val imageAssembler: ImageAssembler = new ImageAssembler()
+ .setInputCol("image")
+ .setOutputCol("image_assembler")
+val candidateLabels = Array(
+ "a photo of a bird",
+ "a photo of a cat",
+ "a photo of a dog",
+ "a photo of a hen",
+ "a photo of a hippo",
+ "a photo of a room",
+ "a photo of a tractor",
+ "a photo of an ostrich",
+ "a photo of an ox")
+val imageClassifier = CLIPForZeroShotClassification
+ .pretrained()
+ .setInputCols("image_assembler")
+ .setOutputCol("label")
+ .setCandidateLabels(candidateLabels)
+val pipeline =
+ new Pipeline().setStages(Array(imageAssembler, imageClassifier)).fit(imageDF).transform(imageDF)
+pipeline
+ .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "label.result")
+ .show(truncate = false)
+```
+
+
+## Results
+
+```bash
++-----------------+-----------------------+
+|image_name |result |
++-----------------+-----------------------+
+|palace.JPEG |[a photo of a room] |
+|egyptian_cat.jpeg|[a photo of a cat] |
+|hippopotamus.JPEG|[a photo of a hippo] |
+|hen.JPEG |[a photo of a hen] |
+|ostrich.JPEG |[a photo of an ostrich]|
+|junco.JPEG |[a photo of a bird] |
+|bluetick.jpg |[a photo of a dog] |
+|chihuahua.jpg |[a photo of a dog] |
+|tractor.JPEG |[a photo of a tractor] |
+|ox.JPEG |[a photo of an ox] |
++-----------------+-----------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|zero_shot_classifier_clip_vit_base_patch32|
+|Compatibility:|Spark NLP 5.2.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[classification]|
+|Language:|en|
+|Size:|392.8 MB|