Instructions to use prithivMLmods/Fashion-Mnist-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Fashion-Mnist-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Fashion-Mnist-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Fashion-Mnist-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Fashion-Mnist-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
```py
|
| 2 |
+
Classification Report:
|
| 3 |
+
precision recall f1-score support
|
| 4 |
+
|
| 5 |
+
T-shirt / top 0.8142 0.9147 0.8615 6000
|
| 6 |
+
Trouser 0.9935 0.9870 0.9902 6000
|
| 7 |
+
Pullover 0.8901 0.8610 0.8753 6000
|
| 8 |
+
Dress 0.9098 0.9300 0.9198 6000
|
| 9 |
+
Coat 0.8636 0.8865 0.8749 6000
|
| 10 |
+
Sandal 0.9857 0.9847 0.9852 6000
|
| 11 |
+
Shirt 0.8076 0.6962 0.7478 6000
|
| 12 |
+
Sneaker 0.9663 0.9695 0.9679 6000
|
| 13 |
+
Bag 0.9779 0.9805 0.9792 6000
|
| 14 |
+
Ankle boot 0.9698 0.9700 0.9699 6000
|
| 15 |
+
|
| 16 |
+
accuracy 0.9180 60000
|
| 17 |
+
macro avg 0.9179 0.9180 0.9172 60000
|
| 18 |
+
weighted avg 0.9179 0.9180 0.9172 60000
|
| 19 |
+
```
|
| 20 |
+
|
| 21 |
+

|