Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use Angy309/swinv2-tiny-patch4-window8-256-prueba2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angy309/swinv2-tiny-patch4-window8-256-prueba2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Angy309/swinv2-tiny-patch4-window8-256-prueba2") 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("Angy309/swinv2-tiny-patch4-window8-256-prueba2") model = AutoModelForImageClassification.from_pretrained("Angy309/swinv2-tiny-patch4-window8-256-prueba2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1671dc1bd8fc3bf14efce887f658b0631655814fc9ee3a41dba8bd182d9b6206
- Size of remote file:
- 110 MB
- SHA256:
- 811dab0cc167d2a0b6543b9c618178b673911c35a2c09a785e4e5f158579290d
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