# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("berng/myclass2")
model = AutoModelForImageClassification.from_pretrained("berng/myclass2")Quick Links
myclass2
This model is a fine-tuned version of microsoft/cvt-13 on the pcuenq/oxford-pets dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
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Model tree for berng/myclass2
Base model
microsoft/cvt-13
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="berng/myclass2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")