Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use moock/swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moock/swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="moock/swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max") 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("moock/swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max") model = AutoModelForImageClassification.from_pretrained("moock/swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bbe2e9f697784818c82866b086f7ad2aa21bb090f8bcfeb0f9069ed3ce0d666
- Size of remote file:
- 4.79 kB
- SHA256:
- 81e9c804b177a239e1066ec42d5a893c7bd6e29d0f1ad2294063020d5d452657
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