Instructions to use galbitang/autotrain-jinvit_sofa_base-94978146242 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galbitang/autotrain-jinvit_sofa_base-94978146242 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="galbitang/autotrain-jinvit_sofa_base-94978146242") 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-jinvit_sofa_base-94978146242") model = AutoModelForImageClassification.from_pretrained("galbitang/autotrain-jinvit_sofa_base-94978146242", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 94978146242
- CO2 Emissions (in grams): 0.0588
Validation Metrics
- Loss: 0.725
- Accuracy: 0.750
- Macro F1: 0.678
- Micro F1: 0.750
- Weighted F1: 0.737
- Macro Precision: 0.746
- Micro Precision: 0.750
- Weighted Precision: 0.752
- Macro Recall: 0.654
- Micro Recall: 0.750
- Weighted Recall: 0.750
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