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Update doc examples feature extractor -> image processor (#20501)

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* Update doc example feature extractor -> image processor

* Apply suggestions from code review
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amyeroberts
2022-11-30 14:50:55 +00:00
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docs/source/en/task_summary.mdx
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@@ -1101,24 +1101,24 @@ Class Egyptian cat with score 0.0239
Class tiger cat with score 0.0229
```
The general process for using a model and feature extractor for image classification is:
The general process for using a model and image processor for image classification is:
1. Instantiate a feature extractor and a model from the checkpoint name.
2. Process the image to be classified with a feature extractor.
1. Instantiate an image processor and a model from the checkpoint name.
2. Process the image to be classified with an image processor.
3. Pass the input through the model and take the `argmax` to retrieve the predicted class.
4. Convert the class id to a class name with `id2label` to return an interpretable result.
<frameworkcontent>
<pt>
```py
>>> from transformers import AutoFeatureExtractor, AutoModelForImageClassification
>>> from transformers import AutoImageProcessor, AutoModelForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
>>> feature_extractor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
>>> model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")
>>> inputs = feature_extractor(image, return_tensors="pt")