Instructions to use sngsfydy/MobileViT_with_Trainer_8_14_2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sngsfydy/MobileViT_with_Trainer_8_14_2023 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sngsfydy/MobileViT_with_Trainer_8_14_2023") 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("sngsfydy/MobileViT_with_Trainer_8_14_2023") model = AutoModelForImageClassification.from_pretrained("sngsfydy/MobileViT_with_Trainer_8_14_2023", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fd6a6181bec13a0474f5dc7536978508f58b597d48263f8d33bbb5f0cbfe748
- Size of remote file:
- 19.9 MB
- SHA256:
- 92fb0e9c04f0cee44199f8e185396aac705becc173d3e66c5059eef7cf69ffa8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.