Instructions to use Arm627/WaspImageRecComap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arm627/WaspImageRecComap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Arm627/WaspImageRecComap") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Arm627/WaspImageRecComap") model = AutoModelForImageClassification.from_pretrained("Arm627/WaspImageRecComap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Arm627/WaspImageRecComap: direct link, hf CLI and curl.
- Browser
- Download file 741 Bytes
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https://huggingface.co/Arm627/WaspImageRecComap/resolve/main/README.md
- Command line
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hf download hf://Arm627/WaspImageRecComap/README.md
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curl -L -o README.md https://huggingface.co/Arm627/WaspImageRecComap/resolve/main/README.md
741 Bytes
| tags: | |
| - autotrain | |
| - vision | |
| - image-classification | |
| datasets: | |
| - Arm627/autotrain-data-wasp_classification_2.0 | |
| 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: 0.8068778967193102 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Binary Classification | |
| - Model ID: 2977085925 | |
| - CO2 Emissions (in grams): 0.8069 | |
| ## Validation Metrics | |
| - Loss: 0.647 | |
| - Accuracy: 0.697 | |
| - Precision: 0.065 | |
| - Recall: 0.667 | |
| - AUC: 0.601 | |
| - F1: 0.118 |