Instructions to use AFZAL0008/LeViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AFZAL0008/LeViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AFZAL0008/LeViT") 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("AFZAL0008/LeViT") model = AutoModelForImageClassification.from_pretrained("AFZAL0008/LeViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
LeViT
This model is a fine-tuned version of facebook/levit-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2414
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6233 | 1.0 | 77 | 0.5909 |
| 0.4740 | 2.0 | 154 | 0.4537 |
| 0.3521 | 3.0 | 231 | 0.5897 |
| 0.2737 | 4.0 | 308 | 1.0422 |
| 0.2395 | 5.0 | 385 | 0.7413 |
| 0.2103 | 6.0 | 462 | 0.7779 |
| 0.1957 | 7.0 | 539 | 0.5417 |
| 0.1613 | 8.0 | 616 | 0.2639 |
| 0.1751 | 9.0 | 693 | 0.6996 |
| 0.1552 | 10.0 | 770 | 1.2414 |
Framework versions
- Transformers 5.22.1
- Pytorch 2.14.1+cu130
- Datasets 4.8.5
- Tokenizers 0.23.3
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Model tree for AFZAL0008/LeViT
Base model
facebook/levit-256