Instructions to use hypha-space/resnet_50_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hypha-space/resnet_50_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hypha-space/resnet_50_trained") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hypha-space/resnet_50_trained") model = AutoModelForImageClassification.from_pretrained("hypha-space/resnet_50_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload ResNetForImageClassification
Browse files- model.safetensors +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 102482856
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2bed2e8885b9bc91eabc023565fd4943e5da5d37baf1d2ae7aabc99d8154139a
|
| 3 |
size 102482856
|