### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationLearned
import requests
from PIL import Image
feature_extractor = PerceiverFeatureExtractor.from_pretrained("addy88/perceiver_image_classifier")
model = PerceiverForImageClassificationLearned.from_pretrained("addy88/perceiver_image_classifier")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# prepare input
encoding = feature_extractor(image, return_tensors="pt")
inputs = encoding.pixel_values
# forward pass
outputs = model(inputs)
logits = outputs.logits
print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
>>> should print Predicted class: tabby, tabby cat
```
apple aquarium_fish baby bear beaver bed bee beetle bicycle bottle bowl boy bridge bus butterfly camel can castle caterpillar cattle chair chimpanzee clock cloud cockroach couch cra crocodile cup dinosaur dolphin elephant flatfish forest fox girl hamster house kangaroo keyboard lamp lawn_mower leopard lion lizard lobster man maple_tree motorcycle mountain mouse mushroom oak_tree orange orchid otter palm_tree pear pickup_truck pine_tree plain plate poppy porcupine possum rabbit raccoon ray road rocket rose sea seal shark shrew skunk skyscraper snail snake spider squirrel streetcar sunflower sweet_pepper table tank telephone television tiger tractor train trout tulip turtle wardrobe whale willow_tree wolf woman worm