Instructions to use Hemg/Wound-Image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Wound-Image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemg/Wound-Image-classification") 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("Hemg/Wound-Image-classification") model = AutoModelForImageClassification.from_pretrained("Hemg/Wound-Image-classification") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 7
Browse files
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 343239356
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f5573d7fd9c617a0fb09c2d2d696fe1a9da9cc1fd138a593557a0217fb84b4fd
|
| 3 |
size 343239356
|
runs/Mar11_11-15-07_33467ebf2898/events.out.tfevents.1710155707.33467ebf2898.370.14
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a05da518f13be7b8339cb8cdf98d8c6e97515a53872b3a78197332675430289
|
| 3 |
+
size 8525
|