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:
- da8ac41a9b4f803ed33523d338839b8fce74228ecb2f9d9f0583370879d84c1c
- Size of remote file:
- 5.05 kB
- SHA256:
- 04bffd223edfc3672f2a824180c413175a091fd40505c6c770e3424cafa7deaa
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