Image Classifiers
Collection
6 items • Updated
How to use MatanBT/swin-tiny-patch4-window7-224-cifar100 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-cifar100")
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-cifar100")
model = AutoModelForImageClassification.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar100")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 |
|---|---|---|---|---|
| 0.7531 | 1.2788 | 500 | 0.7326 | 0.7825 |
| 0.4348 | 2.5575 | 1000 | 0.5393 | 0.8389 |
| 0.3005 | 3.8363 | 1500 | 0.4975 | 0.8536 |
| 0.1651 | 5.1151 | 2000 | 0.4906 | 0.8664 |
| 0.0960 | 6.3939 | 2500 | 0.4844 | 0.8701 |
| 0.0716 | 7.6726 | 3000 | 0.4771 | 0.8767 |
| 0.0538 | 8.9514 | 3500 | 0.4724 | 0.8783 |
Base model
microsoft/swin-tiny-patch4-window7-224