# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("pollux435/ViT_model_output")
model = AutoModelForImageClassification.from_pretrained("pollux435/ViT_model_output")Quick Links
ViT_model_output
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the lewtun/dog_food dataset. It achieves the following results on the evaluation set:
- Loss: 0.4552
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 36 | 0.4552 |
| No log | 2.0 | 72 | 0.1703 |
| No log | 3.0 | 108 | 0.1101 |
| No log | 4.0 | 144 | 0.0927 |
| No log | 5.0 | 180 | 0.0876 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for pollux435/ViT_model_output
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pollux435/ViT_model_output") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")