Instructions to use miittnnss/idk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miittnnss/idk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="miittnnss/idk") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("miittnnss/idk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from miittnnss/idk: direct link, hf CLI and curl.
- Browser
- Download file 787 Bytes
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https://huggingface.co/miittnnss/idk/resolve/main/README.md
- Command line
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hf download hf://miittnnss/idk/README.md
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curl -L -o README.md https://huggingface.co/miittnnss/idk/resolve/main/README.md
787 Bytes
| tags: | |
| - autotrain | |
| - vision | |
| - image-classification | |
| datasets: | |
| - Carlangeloconcepcionrepoyo/autotrain-data-dambuhalang-pogi-scout | |
| 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: 1.7850904815735922 | |
| library_name: transformers | |
| # Model Trained Using AutoTrain | |
| - Problem type: Binary Classification | |
| - Model ID: 2169069849 | |
| - CO2 Emissions (in grams): 1.7851 | |
| ## Validation Metrics | |
| - Loss: 0.026 | |
| - Accuracy: 1.000 | |
| - Precision: 1.000 | |
| - Recall: 1.000 | |
| - AUC: 1.000 | |
| - F1: 1.000 |