Instructions to use OWOW/chesto-at-x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OWOW/chesto-at-x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OWOW/chesto-at-x") 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("OWOW/chesto-at-x") model = AutoModelForImageClassification.from_pretrained("OWOW/chesto-at-x", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metricsg
loss: 1.7624108791351318
f1_macro: 0.09161234101916443
f1_micro: 0.46504065040650405
f1_weighted: 0.363621905317759
precision_macro: 0.12658945013022266
precision_micro: 0.46504065040650405
precision_weighted: 0.3298574193813909
recall_macro: 0.10434538005151346
recall_micro: 0.46504065040650405
recall_weighted: 0.46504065040650405
accuracy: 0.46504065040650405
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