Instructions to use SerdarHelli/ThyroidTumorClassificationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SerdarHelli/ThyroidTumorClassificationModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SerdarHelli/ThyroidTumorClassificationModel") 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("SerdarHelli/ThyroidTumorClassificationModel") model = AutoModelForImageClassification.from_pretrained("SerdarHelli/ThyroidTumorClassificationModel") - Notebooks
- Google Colab
- Kaggle
Commit ·
468349a
1
Parent(s): 6cac197
Update README.md
Browse files
README.md
CHANGED
|
@@ -11,5 +11,5 @@ Thyroid nodule is one of the most common endocrine carcinomas. Due to its higher
|
|
| 11 |
|
| 12 |
In this study, the purpose is the classification of thyroid tumors on ultrasound images with 2 different categories:
|
| 13 |
|
| 14 |
-
- Malign
|
| 15 |
-
- Benign
|
|
|
|
| 11 |
|
| 12 |
In this study, the purpose is the classification of thyroid tumors on ultrasound images with 2 different categories:
|
| 13 |
|
| 14 |
+
- Malign(1)
|
| 15 |
+
- Benign(0)
|