Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use zabir735/swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zabir735/swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="zabir735/swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2") 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("zabir735/swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2") model = AutoModelForImageClassification.from_pretrained("zabir735/swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1734
- Accuracy: 0.9659
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.0433 | 0.9949 | 49 | 0.2793 | 0.9545 |
| 0.0419 | 1.9898 | 98 | 0.1446 | 0.9716 |
| 0.0155 | 2.9848 | 147 | 0.1467 | 0.9773 |
| 0.0011 | 4.0 | 197 | 0.1783 | 0.9602 |
| 0.001 | 4.9746 | 245 | 0.1734 | 0.9659 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for zabir735/swin-tiny-patch4-window7-224-finetuned-batch8-nocrop2
Base model
microsoft/swin-tiny-patch4-window7-224Evaluation results
- Accuracy on imagefolderself-reported0.966