Instructions to use MatanBT/swin-tiny-patch4-window7-224-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatanBT/swin-tiny-patch4-window7-224-cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatanBT/swin-tiny-patch4-window7-224-cifar100") 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("MatanBT/swin-tiny-patch4-window7-224-cifar100") model = AutoModelForImageClassification.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar100") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-cifar100
results: []
swin-tiny-patch4-window7-224-cifar100
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: 0.4724
- Accuracy: 0.8783
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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7531 | 1.2788 | 500 | 0.7326 | 0.7825 |
| 0.4348 | 2.5575 | 1000 | 0.5393 | 0.8389 |
| 0.3005 | 3.8363 | 1500 | 0.4975 | 0.8536 |
| 0.1651 | 5.1151 | 2000 | 0.4906 | 0.8664 |
| 0.0960 | 6.3939 | 2500 | 0.4844 | 0.8701 |
| 0.0716 | 7.6726 | 3000 | 0.4771 | 0.8767 |
| 0.0538 | 8.9514 | 3500 | 0.4724 | 0.8783 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.6.0+cu124
- Datasets 4.8.3
- Tokenizers 0.22.2