Update README.md

README.md

@@ -32,15 +32,15 @@ fine-tuned versions on a task that interests you.
 Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
 
 ```python
-from transformers import AutoFeatureExtractor, BiTForImageClassification
+from transformers import BitImageProcessor, BitForImageClassification
 import torch
 from datasets import load_dataset
 
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 
-feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
-model = BiTForImageClassification.from_pretrained("google/bit-50")
+feature_extractor = BitImageProcessor.from_pretrained("google/bit-50")
+model = BitForImageClassification.from_pretrained("google/bit-50")
 
 inputs = feature_extractor(image, return_tensors="pt")
 
@@ -49,7 +49,8 @@ with torch.no_grad():
 
 # model predicts one of the 1000 ImageNet classes
 predicted_label = logits.argmax(-1).item()
-print(model.config.id2label[predicted_label])
+print(model.config.id2label[predicted_label
+>>> tabby, tabby cat
 ```
 
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/bit).