Instructions to use luisafrancielle/amns with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luisafrancielle/amns with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="luisafrancielle/amns") 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("luisafrancielle/amns") model = AutoModelForImageClassification.from_pretrained("luisafrancielle/amns") - Notebooks
- Google Colab
- Kaggle
amns
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:
- Loss: 0.7099
- Accuracy: 0.8871
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 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 31 | 1.3292 | 0.5574 |
| No log | 2.0 | 62 | 0.9371 | 0.8033 |
| No log | 3.0 | 93 | 0.7407 | 0.8852 |
| 1.2134 | 4.0 | 124 | 0.6463 | 0.9016 |
| 1.2134 | 5.0 | 155 | 0.6189 | 0.9016 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for luisafrancielle/amns
Base model
google/vit-base-patch16-224