Instructions to use JuanMa360/val-vit-kitchen-shapes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuanMa360/val-vit-kitchen-shapes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JuanMa360/val-vit-kitchen-shapes") 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("JuanMa360/val-vit-kitchen-shapes") model = AutoModelForImageClassification.from_pretrained("JuanMa360/val-vit-kitchen-shapes") - Notebooks
- Google Colab
- Kaggle
val-vit-kitchen-shapes
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.4589
- Accuracy: 0.3925
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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 60 | 1.4294 | 0.4393 |
| No log | 2.0 | 120 | 1.4529 | 0.4019 |
| No log | 3.0 | 180 | 1.4798 | 0.4112 |
| No log | 4.0 | 240 | 1.4490 | 0.4206 |
| No log | 5.0 | 300 | 1.4589 | 0.3925 |
Framework versions
- Transformers 4.39.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for JuanMa360/val-vit-kitchen-shapes
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on imagefolderself-reported0.393