Instructions to use facebook/deit-base-distilled-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/deit-base-distilled-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/deit-base-distilled-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-base-distilled-patch16-224") model = AutoModelForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
2ab8949
1
Parent(s): ea2cf46
Add TF weights
Browse filesModel 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=3.612e-05; Maximum crossload hidden layer difference=1.945e-03;
Maximum conversion output difference=3.612e-05; Maximum conversion hidden layer difference=1.945e-03;
- tf_model.h5 +3 -0
tf_model.h5
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oid sha256:6c0d248fcb1b511b3d2a4dc2c6ef06af3b9a034312a49ec0ba7d38d8b4e75e2f
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size 349641024
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