Instructions to use IsaacMwesigwa/footballer-vit-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IsaacMwesigwa/footballer-vit-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="IsaacMwesigwa/footballer-vit-1") 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("IsaacMwesigwa/footballer-vit-1") model = AutoModelForImageClassification.from_pretrained("IsaacMwesigwa/footballer-vit-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Image Classification
- Model trained up to players called up for World Cup 2022.
- Model struggles to recognize more recent players and players in their current clubs(if changed)
Validation Metrics
loss: 2.265369176864624
f1_macro: 0.5847796181041152
f1_micro: 0.5663936400867261
f1_weighted: 0.585042049091963
precision_macro: 0.6516799032153778
precision_micro: 0.5663936400867261
precision_weighted: 0.6520343151781112
recall_macro: 0.5662396808967206
recall_micro: 0.5663936400867261
recall_weighted: 0.5663936400867261
accuracy: 0.5663936400867261
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