Instructions to use micole66/autotrain-pachyderms-v3-2088867198 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use micole66/autotrain-pachyderms-v3-2088867198 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="micole66/autotrain-pachyderms-v3-2088867198") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("micole66/autotrain-pachyderms-v3-2088867198", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - vision | |
| - image-classification | |
| datasets: | |
| - micole66/autotrain-data-pachyderms-v3 | |
| 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: 1.5875017032028493 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Binary Classification | |
| - Model ID: 2088867198 | |
| - CO2 Emissions (in grams): 1.5875 | |
| ## Validation Metrics | |
| - Loss: 0.020 | |
| - Accuracy: 1.000 | |
| - Precision: 1.000 | |
| - Recall: 1.000 | |
| - AUC: 1.000 | |
| - F1: 1.000 |