Instructions to use devboop/vit-base-patch16-224-cl-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devboop/vit-base-patch16-224-cl-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="devboop/vit-base-patch16-224-cl-v1") 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("devboop/vit-base-patch16-224-cl-v1") model = AutoModelForImageClassification.from_pretrained("devboop/vit-base-patch16-224-cl-v1") - Notebooks
- Google Colab
- Kaggle
vit-base-patch16-224-cl-v1
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.4053
- Accuracy: 0.5027
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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.6762 | 1.0 | 353 | 3.4472 | 0.3691 |
| 2.8516 | 2.0 | 706 | 2.5892 | 0.4738 |
| 2.6887 | 3.0 | 1059 | 2.4053 | 0.5027 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for devboop/vit-base-patch16-224-cl-v1
Base model
google/vit-base-patch16-224