Patacognition
Collection
All ViTs, datasets and spaces created for the sole purpose of patacón recognition. • 8 items • Updated
How to use frncscp/patacoswin with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="frncscp/patacoswin")
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("frncscp/patacoswin")
model = AutoModelForImageClassification.from_pretrained("frncscp/patacoswin")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 |
|---|---|---|---|---|
| No log | 0.96 | 6 | 0.4003 | 0.875 |
| 0.5091 | 1.92 | 12 | 0.1308 | 0.9886 |
| 0.5091 | 2.88 | 18 | 0.0522 | 0.9886 |
| 0.1585 | 4.0 | 25 | 0.0203 | 1.0 |
| 0.0925 | 4.96 | 31 | 0.0156 | 1.0 |
| 0.0925 | 5.92 | 37 | 0.0196 | 1.0 |
| 0.0539 | 6.88 | 43 | 0.0095 | 1.0 |
| 0.0397 | 8.0 | 50 | 0.0089 | 1.0 |
| 0.0397 | 8.96 | 56 | 0.0089 | 1.0 |
| 0.0378 | 9.6 | 60 | 0.0090 | 1.0 |
Base model
microsoft/swin-tiny-patch4-window7-224