Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon") 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("mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon") model = AutoModelForImageClassification.from_pretrained("mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon
This model is a fine-tuned version of 100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0971
- Accuracy: 0.9652
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2504 | 0.99 | 20 | 0.1401 | 0.9512 |
| 0.2051 | 1.98 | 40 | 0.1083 | 0.9652 |
| 0.1894 | 2.96 | 60 | 0.0939 | 0.9652 |
| 0.1115 | 4.0 | 81 | 0.0880 | 0.9686 |
| 0.117 | 4.94 | 100 | 0.0971 | 0.9652 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for mansee/swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon
Base model
microsoft/swin-tiny-patch4-window7-224Evaluation results
- Accuracy on imagefolderself-reported0.965