# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("sgenzer/seven-segment-led")
model = AutoModelForImageClassification.from_pretrained("sgenzer/seven-segment-led", device_map="auto")Quick Links
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 88543143697
- CO2 Emissions (in grams): 0.6090
Validation Metrics
- Loss: 0.503
- Accuracy: 0.868
- Macro F1: 0.862
- Micro F1: 0.868
- Weighted F1: 0.871
- Macro Precision: 0.871
- Micro Precision: 0.868
- Weighted Precision: 0.883
- Macro Recall: 0.862
- Micro Recall: 0.868
- Weighted Recall: 0.868
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sgenzer/seven-segment-led") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")