chunked/nltk_chunking/tasks_image_classification/chunk_53.txt

Instantiate a pipeline for image classification with your model, and pass your image to it:

from transformers import pipeline
classifier = pipeline("image-classification", model="my_awesome_food_model")
classifier(image)
[{'score': 0.31856709718704224, 'label': 'beignets'},
 {'score': 0.015232225880026817, 'label': 'bruschetta'},
 {'score': 0.01519392803311348, 'label': 'chicken_wings'},
 {'score': 0.013022331520915031, 'label': 'pork_chop'},
 {'score': 0.012728818692266941, 'label': 'prime_rib'}]

You can also manually replicate the results of the pipeline if you'd like:

Load an image processor to preprocess the image and return the input as PyTorch tensors:

from transformers import AutoImageProcessor
import torch
image_processor = AutoImageProcessor.from_pretrained("my_awesome_food_model")
inputs = image_processor(image, return_tensors="pt")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained("my_awesome_food_model")
with torch.no_grad():
     logits = model(**inputs).logits

Get the predicted label with the highest probability, and use the model's id2label mapping to convert it to a label:

predicted_label = logits.argmax(-1).item()
model.config.id2label[predicted_label]
'beignets'

Load an image processor to preprocess the image and return the input as TensorFlow tensors:

from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained("MariaK/food_classifier")
inputs = image_processor(image, return_tensors="tf")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForImageClassification
model = TFAutoModelForImageClassification.from_pretrained("MariaK/food_classifier")
logits = model(**inputs).logits

Get the predicted label with the highest probability, and use the model's id2label mapping to convert it to a label:

predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
model.config.id2label[predicted_class_id]
'beignets'