Instructions to use timm/davit_base.msft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/davit_base.msft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/davit_base.msft_in1k", pretrained=True) - Transformers
How to use timm/davit_base.msft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/davit_base.msft_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/davit_base.msft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cd12f8bd194e17d9e7faa4d34a7634a185b2f2d20f8cdf543a2b83831a43ed84
|
| 3 |
+
size 351859600
|