Instructions to use polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new") 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("polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new") model = AutoModelForImageClassification.from_pretrained("polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224-lcbsi-wbc-new
This model is a fine-tuned version of polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc on the WBC dataset. It achieves the following results on the evaluation set:
- Loss: 0.0457
- Accuracy: 0.992
- Precision: 0.9920
- Recall: 0.992
- F1: 0.9920
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: 0.0002562
- 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 | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0936 | 0.98 | 27 | 0.0724 | 0.984 | 0.9841 | 0.984 | 0.9840 |
| 0.0276 | 1.98 | 54 | 0.0768 | 0.984 | 0.9841 | 0.984 | 0.9839 |
| 0.0133 | 2.98 | 81 | 0.0457 | 0.992 | 0.9920 | 0.992 | 0.9920 |
Framework versions
- Transformers 4.25.1
- Pytorch 2.0.0.dev20230107
- Datasets 2.8.0
- Tokenizers 0.13.2
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