Instructions to use apple/mobilevit-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/mobilevit-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="apple/mobilevit-small") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small") model = AutoModelForImageClassification.from_pretrained("apple/mobilevit-small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Add TF weights
Model converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=1.287e-04; Maximum crossload hidden layer difference=4.578e-05; Maximum conversion output difference=1.287e-04; Maximum conversion hidden layer difference=4.578e-05; CAUTION: The maximum admissible error was manually increased to 0.0002!3a6e59e
-
add model
0d75931
-
add Core ML model
3032fc8 -
add model card
881ba66 -
add model
f132b44 -
add model
533e346 -
initial commit
d01518f