Instructions to use facebook/deit-tiny-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/deit-tiny-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/deit-tiny-patch16-224") 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("facebook/deit-tiny-patch16-224") model = AutoModelForImageClassification.from_pretrained("facebook/deit-tiny-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
Model converted by the transformers' pt_to_tf CLI. All converted model outputs and hidden layers were validated against its Pytorch counterpart.
Maximum crossload output difference=1.502e-05; Maximum crossload hidden layer difference=6.371e-04;
Maximum conversion output difference=1.502e-05; Maximum conversion hidden layer difference=6.371e-04;
List of maximum output differences above the threshold (1e-19):
logits: 1.502e-05
List of maximum hidden layer differences above the threshold (1e-19):
hidden_states[0]: 6.199e-06
hidden_states[1]: 8.106e-06
hidden_states[2]: 1.155e-05
hidden_states[3]: 1.472e-05
hidden_states[4]: 2.205e-05
hidden_states[5]: 2.849e-05
hidden_states[6]: 5.364e-05
hidden_states[7]: 1.538e-04
hidden_states[8]: 2.689e-04
hidden_states[9]: 3.753e-04
hidden_states[10]: 5.169e-04
hidden_states[11]: 6.371e-04
hidden_states[12]: 6.227e-04