# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("bombshelll/swin-brain-plane-classification-fold1")
model = AutoModelForImageClassification.from_pretrained("bombshelll/swin-brain-plane-classification-fold1")Quick Links
swin-brain-plane-classification-fold1
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: 0.0842
- Accuracy: 0.9873
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2583 | 0.8 | 3 | 0.8256 | 0.7911 |
| 0.6521 | 1.8667 | 7 | 0.4384 | 0.9304 |
| 0.3974 | 2.9333 | 11 | 0.2148 | 0.9557 |
| 0.305 | 4.0 | 15 | 0.1304 | 0.9747 |
| 0.3045 | 4.8 | 18 | 0.1032 | 0.9810 |
| 0.1542 | 5.8667 | 22 | 0.1088 | 0.9684 |
| 0.1853 | 6.9333 | 26 | 0.0930 | 0.9873 |
| 0.1554 | 8.0 | 30 | 0.0842 | 0.9873 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for bombshelll/swin-brain-plane-classification-fold1
Base model
microsoft/swin-tiny-patch4-window7-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bombshelll/swin-brain-plane-classification-fold1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")