Instructions to use Visual-Attention-Network/van-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Visual-Attention-Network/van-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Visual-Attention-Network/van-tiny") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("Visual-Attention-Network/van-tiny", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add model
Browse files- pytorch_model.bin +2 -2
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:df40b091c7f2067c8086d24c6fd9b7403ce773593f2a6df02ad035e0de575ec0
|
| 3 |
+
size 16613913
|