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Update old existing feature extractor references (#24552)

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* Update old existing feature extractor references

* Typo

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

* Address comments from review - update 'feature extractor'
Co-authored by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
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amyeroberts
2023-06-29 10:17:36 +01:00
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parent 10c2ac7bc6
commit ae454f41d4
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docs/source/es/tasks/image_classification.md
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@@ -73,12 +73,12 @@ Cada clase de alimento - o label - corresponde a un número; `79` indica una cos
## Preprocesa
Carga el feature extractor de ViT para procesar la imagen en un tensor:
Carga el image processor de ViT para procesar la imagen en un tensor:
```py
>>> from transformers import AutoFeatureExtractor
>>> from transformers import AutoImageProcessor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
```
Aplica varias transformaciones de imagen al dataset para hacer el modelo más robusto contra el overfitting. En este caso se utilizará el módulo [`transforms`](https://pytorch.org/vision/stable/transforms.html) de torchvision. Recorta una parte aleatoria de la imagen, cambia su tamaño y normalízala con la media y la desviación estándar de la imagen:
@@ -86,8 +86,8 @@ Aplica varias transformaciones de imagen al dataset para hacer el modelo más ro
```py
>>> from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
>>> normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
>>> _transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])
>>> normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
>>> _transforms = Compose([RandomResizedCrop(image_processor.size["height"]), ToTensor(), normalize])
```
Crea una función de preprocesamiento que aplique las transformaciones y devuelva los `pixel_values` - los inputs al modelo - de la imagen:
@@ -160,7 +160,7 @@ Al llegar a este punto, solo quedan tres pasos:
... data_collator=data_collator,
... train_dataset=food["train"],
... eval_dataset=food["test"],
... tokenizer=feature_extractor,
... tokenizer=image_processor,
... )
>>> trainer.train()