Instructions to use hazardous/swin-tiny-patch4-window7-224_isl-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hazardous/swin-tiny-patch4-window7-224_isl-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hazardous/swin-tiny-patch4-window7-224_isl-finetuned") 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("hazardous/swin-tiny-patch4-window7-224_isl-finetuned") model = AutoModelForImageClassification.from_pretrained("hazardous/swin-tiny-patch4-window7-224_isl-finetuned") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224_isl-finetuned
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- 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: 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 |
|---|---|---|---|---|
| 0.0466 | 1.0 | 295 | 0.0004 | 1.0 |
| 0.0234 | 2.0 | 590 | 0.0001 | 1.0 |
| 0.0191 | 3.0 | 885 | 0.0000 | 1.0 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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