Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use grevalby/grevalby-project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grevalby/grevalby-project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="grevalby/grevalby-project") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("grevalby/grevalby-project") model = AutoModelForImageClassification.from_pretrained("grevalby/grevalby-project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
grevalby-project
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: 1.4472
- Accuracy: 0.5563
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 1.4116 | 0.55 |
| No log | 2.0 | 80 | 1.4192 | 0.5375 |
| No log | 3.0 | 120 | 1.3950 | 0.5563 |
| No log | 4.0 | 160 | 1.4198 | 0.55 |
| No log | 5.0 | 200 | 1.4346 | 0.5625 |
| No log | 6.0 | 240 | 1.4440 | 0.55 |
| No log | 7.0 | 280 | 1.4472 | 0.5563 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for grevalby/grevalby-project
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on imagefolderself-reported0.556