Instructions to use Afzalsiiit/SwimV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Afzalsiiit/SwimV2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Afzalsiiit/SwimV2") 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("Afzalsiiit/SwimV2") model = AutoModelForImageClassification.from_pretrained("Afzalsiiit/SwimV2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: microsoft/swin-tiny-patch4-window7-224 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| model-index: | |
| - name: SwimV2 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # SwimV2 | |
| This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0290 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.2588 | 1.0 | 77 | 0.0491 | | |
| | 0.1207 | 2.0 | 154 | 0.0314 | | |
| | 0.1638 | 3.0 | 231 | 0.0820 | | |
| | 0.0741 | 4.0 | 308 | 0.0300 | | |
| | 0.0911 | 5.0 | 385 | 0.0147 | | |
| | 0.0837 | 6.0 | 462 | 0.0227 | | |
| | 0.0634 | 7.0 | 539 | 0.0381 | | |
| | 0.0497 | 8.0 | 616 | 0.0427 | | |
| | 0.0371 | 9.0 | 693 | 0.0250 | | |
| | 0.0345 | 10.0 | 770 | 0.0290 | | |
| ### Framework versions | |
| - Transformers 5.12.1 | |
| - Pytorch 2.11.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |