# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("CVPROJ25/FINETUNED")
model = AutoModelForImageClassification.from_pretrained("CVPROJ25/FINETUNED", device_map="auto")Quick Links
FINETUNED
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9199
- Accuracy: 0.774
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: 5e-05
- train_batch_size: 512
- eval_batch_size: 16
- 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8241 | 1.0 | 16 | 0.8922 | 0.773 |
| 0.3997 | 2.0 | 32 | 0.9019 | 0.7705 |
| 0.2905 | 3.0 | 48 | 0.9199 | 0.774 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.5.1
- Datasets 4.6.1
- Tokenizers 0.22.1
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Model tree for CVPROJ25/FINETUNED
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CVPROJ25/FINETUNED") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")