Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep 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-Lego-v2-3ep 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-Lego-v2-3ep") 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-Lego-v2-3ep") model = AutoModelForImageClassification.from_pretrained("Angy309/swinv2-tiny-patch4-window8-256-Lego-v2-3ep") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97e532d3efcd544db8125bbaaa0ed26bf2606b8c7a652bbb17bd1ad4aac9a31d
- Size of remote file:
- 5.05 kB
- SHA256:
- 1bf33fa8c46d863410a3786127630a94677ec0cc768fb609ebc518a5f0e133b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.