Designing Network Design Spaces
Paper • 2003.13678 • Published • 2
How to use facebook/regnet-y-10b-seer with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-feature-extraction", model="facebook/regnet-y-10b-seer") # Load model directly
from transformers import AutoImageProcessor, AutoModel
processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-10b-seer")
model = AutoModel.from_pretrained("facebook/regnet-y-10b-seer")This gigantic model is a scale up RegNetY model trained on one billion uncurated Instagram images.
Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team.
You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.
Here is how to use this model:
>>> from transformers import AutoFeatureExtractor, RegNetModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-10b-seer")
>>> model = RegNetModel.from_pretrained("facebook/regnet-y-10b-seer")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1088, 7, 7]
For more code examples, we refer to the documentation.