Instructions to use Modotte/AIRealNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Modotte/AIRealNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Modotte/AIRealNet") 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("Modotte/AIRealNet") model = AutoModelForImageClassification.from_pretrained("Modotte/AIRealNet") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -25,10 +25,10 @@ tags:
|
|
| 25 |
|
| 26 |
# XenArcAI/AIRealNet
|
| 27 |
|
| 28 |
-
**Model type:** Image Classification (Binary)
|
| 29 |
-
**Task:** AI-generated vs Human image detection
|
| 30 |
-
**Base model:** `Microsoft/swinv2-tiny-patch4-window16-256`
|
| 31 |
-
**Fine-tuned by:** `Parveshiiii/AI-vs-Real` dataset split
|
| 32 |
|
| 33 |
---
|
| 34 |
|
|
|
|
| 25 |
|
| 26 |
# XenArcAI/AIRealNet
|
| 27 |
|
| 28 |
+
**Model type:** Image Classification (Binary)
|
| 29 |
+
**Task:** AI-generated vs Human image detection
|
| 30 |
+
**Base model:** `Microsoft/swinv2-tiny-patch4-window16-256`
|
| 31 |
+
**Fine-tuned by:** `Parveshiiii/AI-vs-Real` dataset split
|
| 32 |
|
| 33 |
---
|
| 34 |
|