Instructions to use swww/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swww/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="swww/test") 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("swww/test") model = AutoModelForImageClassification.from_pretrained("swww/test") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("swww/test")
model = AutoModelForImageClassification.from_pretrained("swww/test")Quick Links
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2928085012
- CO2 Emissions (in grams): 1.9626
Validation Metrics
- Loss: 0.226
- Accuracy: 0.925
- Macro F1: 0.925
- Micro F1: 0.925
- Weighted F1: 0.925
- Macro Precision: 0.929
- Micro Precision: 0.925
- Weighted Precision: 0.929
- Macro Recall: 0.925
- Micro Recall: 0.925
- Weighted Recall: 0.925
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="swww/test") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")