Instructions to use siddheshtv/BlockNet10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use siddheshtv/BlockNet10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="siddheshtv/BlockNet10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import BlockNet10 model = BlockNet10.from_pretrained("siddheshtv/BlockNet10", dtype="auto") - Notebooks
- Google Colab
- Kaggle
siddheshtv commited on
Commit ·
4dd8f2f
1
Parent(s): fc94346
add metadata
Browse files
README.md
CHANGED
|
@@ -8,7 +8,7 @@ language: en
|
|
| 8 |
framework: pytorch
|
| 9 |
metrics:
|
| 10 |
- accuracy: 75.43
|
| 11 |
-
license_name:
|
| 12 |
datasets:
|
| 13 |
- CIFAR-10
|
| 14 |
---
|
|
|
|
| 8 |
framework: pytorch
|
| 9 |
metrics:
|
| 10 |
- accuracy: 75.43
|
| 11 |
+
license_name: mit
|
| 12 |
datasets:
|
| 13 |
- CIFAR-10
|
| 14 |
---
|