Instructions to use creative-graphic-design/BASNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use creative-graphic-design/BASNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="creative-graphic-design/BASNet", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("creative-graphic-design/BASNet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files
image_processing_basnet.py
CHANGED
|
@@ -6,8 +6,7 @@ import torch
|
|
| 6 |
from PIL import Image
|
| 7 |
from PIL.Image import Image as PilImage
|
| 8 |
from torchvision import transforms
|
| 9 |
-
from transformers.
|
| 10 |
-
from transformers.image_processing_utils import BaseImageProcessor
|
| 11 |
from transformers.image_utils import ImageInput
|
| 12 |
|
| 13 |
|
|
|
|
| 6 |
from PIL import Image
|
| 7 |
from PIL.Image import Image as PilImage
|
| 8 |
from torchvision import transforms
|
| 9 |
+
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
|
|
|
|
| 10 |
from transformers.image_utils import ImageInput
|
| 11 |
|
| 12 |
|