Instructions to use hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier") - Notebooks
- Google Colab
- Kaggle
File size: 131 Bytes
c67b9f1 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:a281f24ec903fa189a108dbf893eaf560949aadf1c4032c7c39135fb3bee3b79
size 121892
|