Instructions to use Ailyth/2_Labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ailyth/2_Labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Ailyth/2_Labels") 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("Ailyth/2_Labels") model = AutoModelForImageClassification.from_pretrained("Ailyth/2_Labels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - vision | |
| - image-classification | |
| datasets: | |
| - Ailyth/autotrain-data-2labels | |
| widget: | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg | |
| example_title: Tiger | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg | |
| example_title: Teapot | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg | |
| example_title: Palace | |
| co2_eq_emissions: | |
| emissions: 2.038789255434584 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Binary Classification | |
| - Model ID: 3268491180 | |
| - CO2 Emissions (in grams): 2.0388 | |
| ## Validation Metrics | |
| - Loss: 0.044 | |
| - Accuracy: 0.970 | |
| - Precision: 0.966 | |
| - Recall: 0.982 | |
| - AUC: 0.998 | |
| - F1: 0.974 |