enterprise-explorers/oxford-pets
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How to use shreydan/vit-base-oxford-iiit-pets with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="shreydan/vit-base-oxford-iiit-pets")
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("shreydan/vit-base-oxford-iiit-pets")
model = AutoModelForImageClassification.from_pretrained("shreydan/vit-base-oxford-iiit-pets")This model is a fine-tuned version of google/vit-base-patch16-224 on the pcuenq/oxford-pets 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 |
|---|---|---|---|---|
| 1.7025 | 1.0 | 185 | 0.3849 | 0.9242 |
| 0.2944 | 2.0 | 370 | 0.2704 | 0.9337 |
| 0.2129 | 3.0 | 555 | 0.2417 | 0.9378 |
| 0.1761 | 4.0 | 740 | 0.2305 | 0.9350 |
| 0.157 | 5.0 | 925 | 0.2281 | 0.9378 |
Base model
google/vit-base-patch16-224