Instructions to use hf-internal-testing/tiny-random-vision_perceiver_conv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-vision_perceiver_conv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-vision_perceiver_conv") 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-vision_perceiver_conv") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vision_perceiver_conv") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
Browse files[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).
- onnx/model.onnx +3 -0
onnx/model.onnx
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size 1107625
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