Instructions to use timm/test_efficientnet_ln.r160_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/test_efficientnet_ln.r160_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/test_efficientnet_ln.r160_in1k", pretrained=True) - Transformers
How to use timm/test_efficientnet_ln.r160_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/test_efficientnet_ln.r160_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/test_efficientnet_ln.r160_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add test vectors
Browse files- test/owl_tensors.safetensors +3 -0
- test/rand_tensors.safetensors +3 -0
- test/test_owl.jpg +0 -0
test/owl_tensors.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:36975da79097a9ae4c8f99c8d8e88f040683894158a259b893e7ef980ff37f10
|
| 3 |
+
size 338136
|
test/rand_tensors.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:212011706c3b9844e94e3406ada75b4c20b455bddb5a8141eade183efa2e567a
|
| 3 |
+
size 1351616
|
test/test_owl.jpg
ADDED
|