Instructions to use hypha-space/lenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hypha-space/lenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hypha-space/lenet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hypha-space/lenet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_lenet.py +1 -1
preprocessor_lenet.py
CHANGED
|
@@ -28,7 +28,7 @@ class LeNetProcessor(BaseImageProcessor):
|
|
| 28 |
images = [images]
|
| 29 |
|
| 30 |
transform = v2.Compose([
|
| 31 |
-
v2.
|
| 32 |
v2.ToDtype(torch.float32, scale=True),
|
| 33 |
v2.Normalize(
|
| 34 |
mean=[0.1307],
|
|
|
|
| 28 |
images = [images]
|
| 29 |
|
| 30 |
transform = v2.Compose([
|
| 31 |
+
v2.Resize(size=(28, 28), antialias=True),
|
| 32 |
v2.ToDtype(torch.float32, scale=True),
|
| 33 |
v2.Normalize(
|
| 34 |
mean=[0.1307],
|