Instructions to use vuongnhathien/test-seed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuongnhathien/test-seed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vuongnhathien/test-seed") 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("vuongnhathien/test-seed") model = AutoModelForImageClassification.from_pretrained("vuongnhathien/test-seed") - Notebooks
- Google Colab
- Kaggle
test-seed
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window16-256 on the don't care dataset. It achieves the following results on the evaluation set:
- Loss: 0.6797
- Accuracy: 0.75
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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 10 | 0.6739 | 0.7375 |
| No log | 2.0 | 20 | 0.4469 | 0.875 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for vuongnhathien/test-seed
Base model
microsoft/swinv2-tiny-patch4-window16-256