ConvNextForImageClassification is an image classifier based on ConvNet models.

The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.

Pretrained models can be loaded with pretrained of the companion object:

val imageClassifier = ConvNextForImageClassification.pretrained()
  .setInputCols("image_assembler")
  .setOutputCol("class")

The default model is "image_classifier_convnext_tiny_224_local", if no name is provided.

For available pretrained models please see the Models Hub.

Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see ConvNextForImageClassificationTestSpec.

References:

A ConvNet for the 2020s

Paper Abstract:

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.

Example

import com.johnsnowlabs.nlp.annotator._
import com.johnsnowlabs.nlp.ImageAssembler
import org.apache.spark.ml.Pipeline

val imageDF: DataFrame = spark.read
  .format("image")
  .option("dropInvalid", value = true)
  .load("src/test/resources/image/")

val imageAssembler = new ImageAssembler()
  .setInputCol("image")
  .setOutputCol("image_assembler")

val imageClassifier = ConvNextForImageClassification
  .pretrained()
  .setInputCols("image_assembler")
  .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
val pipelineDF = pipeline.fit(imageDF).transform(imageDF)

pipelineDF
  .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result")
  .show(truncate = false)
+-----------------+----------------------------------------------------------+
|image_name       |result                                                    |
+-----------------+----------------------------------------------------------+
|palace.JPEG      |[palace]                                                  |
|egyptian_cat.jpeg|[tabby, tabby cat]                                        |
|hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]|
|hen.JPEG         |[hen]                                                     |
|ostrich.JPEG     |[ostrich, Struthio camelus]                               |
|junco.JPEG       |[junco, snowbird]                                         |
|bluetick.jpg     |[bluetick]                                                |
|chihuahua.jpg    |[Chihuahua]                                               |
|tractor.JPEG     |[tractor]                                                 |
|ox.JPEG          |[ox]                                                      |
+-----------------+----------------------------------------------------------+