# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("TTNVXX/BokehOrNot")
model = AutoModelForImageClassification.from_pretrained("TTNVXX/BokehOrNot", device_map="auto")Quick Links
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metricsg
loss: 0.3941328525543213
f1_macro: 0.8130457113507962
f1_micro: 0.8355263157894737
f1_weighted: 0.8288865461033169
precision_macro: 0.8533012943450432
precision_micro: 0.8355263157894737
precision_weighted: 0.8434833671575431
recall_macro: 0.8000841750841751
recall_micro: 0.8355263157894737
recall_weighted: 0.8355263157894737
accuracy: 0.8355263157894737
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TTNVXX/BokehOrNot") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")