Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2") model = AutoModelForImageClassification.from_pretrained("dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 201 Bytes
f22cf1c | 1 2 3 4 5 6 7 8 | {
"epoch": 20.0,
"eval_accuracy": 0.984313725490196,
"eval_loss": 0.07451473921537399,
"eval_runtime": 5.114,
"eval_samples_per_second": 49.863,
"eval_steps_per_second": 1.564
} |