Image Classifiers
Collection
6 items • Updated
How to use MatanBT/swin-tiny-patch4-window7-224-cifar10 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="MatanBT/swin-tiny-patch4-window7-224-cifar10")
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("MatanBT/swin-tiny-patch4-window7-224-cifar10")
model = AutoModelForImageClassification.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar10")# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar10")
model = AutoModelForImageClassification.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar10")This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1430 | 1.2788 | 500 | 0.1258 | 0.9588 |
| 0.0699 | 2.5575 | 1000 | 0.1101 | 0.9676 |
| 0.0489 | 3.8363 | 1500 | 0.1001 | 0.9713 |
| 0.0234 | 5.1151 | 2000 | 0.1054 | 0.9734 |
| 0.0136 | 6.3939 | 2500 | 0.0970 | 0.98 |
| 0.0080 | 7.6726 | 3000 | 0.0995 | 0.9803 |
| 0.0059 | 8.9514 | 3500 | 0.1005 | 0.9799 |
Base model
microsoft/swin-tiny-patch4-window7-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatanBT/swin-tiny-patch4-window7-224-cifar10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")