Instructions to use Rageshhf/fine-tuned-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rageshhf/fine-tuned-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rageshhf/fine-tuned-model") 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("Rageshhf/fine-tuned-model") model = AutoModelForImageClassification.from_pretrained("Rageshhf/fine-tuned-model") - Notebooks
- Google Colab
- Kaggle
fine-tuned-model
This model is a fine-tuned version of google/vit-base-patch16-224 on the Falah/Alzheimer_MRI dataset. It achieves the following results on the evaluation set:
- Loss: 0.8720
- Accuracy: 0.5742
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9696 | 1.0 | 256 | 0.8925 | 0.5781 |
| 0.9141 | 2.0 | 512 | 0.8447 | 0.5938 |
| 0.8669 | 3.0 | 768 | 0.8378 | 0.6035 |
| 0.8356 | 4.0 | 1024 | 0.8236 | 0.5938 |
| 0.8529 | 5.0 | 1280 | 0.8206 | 0.6074 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for Rageshhf/fine-tuned-model
Base model
google/vit-base-patch16-224