Image Classification
Transformers
TensorBoard
Safetensors
vit
fruits-360
vision-transformer
Generated from Trainer
Instructions to use Granitagushi/vit-base-fruits-360 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Granitagushi/vit-base-fruits-360 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Granitagushi/vit-base-fruits-360") 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("Granitagushi/vit-base-fruits-360") model = AutoModelForImageClassification.from_pretrained("Granitagushi/vit-base-fruits-360") - Notebooks
- Google Colab
- Kaggle
vit-base-fruits-360
This model is a fine-tuned version of google/vit-base-patch16-224 on the PedroSampaio/fruits-360 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
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 | 218 | 0.0045 | 1.0 |
| No log | 2.0 | 436 | 0.0020 | 1.0 |
| 0.0566 | 3.0 | 654 | 0.0013 | 1.0 |
| 0.0566 | 4.0 | 872 | 0.0010 | 1.0 |
| 0.0013 | 5.0 | 1090 | 0.0010 | 1.0 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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Model tree for Granitagushi/vit-base-fruits-360
Base model
google/vit-base-patch16-224