Instructions to use Kushagra07/autotrain-swinv2-base-patch4-window8-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushagra07/autotrain-swinv2-base-patch4-window8-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kushagra07/autotrain-swinv2-base-patch4-window8-256") 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("Kushagra07/autotrain-swinv2-base-patch4-window8-256") model = AutoModelForImageClassification.from_pretrained("Kushagra07/autotrain-swinv2-base-patch4-window8-256") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metrics
loss: 0.21217399835586548
f1_macro: 0.8801923417646881
f1_micro: 0.9320587231136906
f1_weighted: 0.9322151264732859
precision_macro: 0.9267115227700036
precision_micro: 0.9320587231136906
precision_weighted: 0.9357267323781668
recall_macro: 0.8522160392320227
recall_micro: 0.9320587231136906
recall_weighted: 0.9320587231136906
accuracy: 0.9320587231136906
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