Instructions to use galbitang/autotrain-bed_frame_style_classification-94482146114 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galbitang/autotrain-bed_frame_style_classification-94482146114 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="galbitang/autotrain-bed_frame_style_classification-94482146114") 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("galbitang/autotrain-bed_frame_style_classification-94482146114") model = AutoModelForImageClassification.from_pretrained("galbitang/autotrain-bed_frame_style_classification-94482146114") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 94482146114
- CO2 Emissions (in grams): 0.0920
Validation Metrics
- Loss: 0.544
- Accuracy: 0.824
- Macro F1: 0.820
- Micro F1: 0.824
- Weighted F1: 0.822
- Macro Precision: 0.829
- Micro Precision: 0.824
- Weighted Precision: 0.825
- Macro Recall: 0.816
- Micro Recall: 0.824
- Weighted Recall: 0.824
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