Instructions to use tejp/fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tejp/fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tejp/fine-tuned") 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("tejp/fine-tuned") model = AutoModelForImageClassification.from_pretrained("tejp/fine-tuned") - Notebooks
- Google Colab
- Kaggle
fine-tuned
This model is a fine-tuned version of google/vit-base-patch16-224 on the custom_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 2.0068
- Accuracy: 0.2857
- F1: 0.2030
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
- Downloads last month
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Model tree for tejp/fine-tuned
Base model
google/vit-base-patch16-224Evaluation results
- Accuracy on custom_datasetself-reported0.286
- F1 on custom_datasetself-reported0.203