Instructions to use hf-internal-testing/tiny-random-ImageGPTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ImageGPTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ImageGPTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ImageGPTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ImageGPTForImageClassification") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#2
by Xenova HF Staff - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:799cae7f2c716024923075c39eec7f9e2ffc3438c9b238fbf8dd4dd7e8c1805e
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size 1557930
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