Instructions to use deepmind/vision-perceiver-conv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepmind/vision-perceiver-conv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="deepmind/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("deepmind/vision-perceiver-conv") model = AutoModelForImageClassification.from_pretrained("deepmind/vision-perceiver-conv") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3962023701856cab490a052ae03a6c276b9b7b127f1027dfc955e5d0c0e79316
- Size of remote file:
- 195 MB
- SHA256:
- 50d6ac6768785e8e64792974f8d35501132733104a80fefd771e939a78e6837f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.