Instructions to use AFZAL0008/SwimV1Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AFZAL0008/SwimV1Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AFZAL0008/SwimV1Base") 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("AFZAL0008/SwimV1Base") model = AutoModelForImageClassification.from_pretrained("AFZAL0008/SwimV1Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AFZAL0008/SwimV1Base: direct link, hf CLI and curl.
- Browser
- Download file 1.94 kB
-
https://huggingface.co/AFZAL0008/SwimV1Base/resolve/main/README.md
- Command line
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hf download hf://AFZAL0008/SwimV1Base/README.md
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curl -L -o README.md https://huggingface.co/AFZAL0008/SwimV1Base/resolve/main/README.md
1.94 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: microsoft/swin-base-patch4-window12-384 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| model-index: | |
| - name: SwimV1Base | |
| 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. --> | |
| # SwimV1Base | |
| This model is a fine-tuned version of [microsoft/swin-base-patch4-window12-384](https://huggingface.co/microsoft/swin-base-patch4-window12-384) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0041 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.2296 | 1.0 | 77 | 0.0181 | | |
| | 0.1378 | 2.0 | 154 | 0.0063 | | |
| | 0.0940 | 3.0 | 231 | 0.1362 | | |
| | 0.0890 | 4.0 | 308 | 0.0164 | | |
| | 0.0590 | 5.0 | 385 | 0.0008 | | |
| | 0.0474 | 6.0 | 462 | 0.0347 | | |
| | 0.0558 | 7.0 | 539 | 0.0344 | | |
| | 0.0430 | 8.0 | 616 | 0.0121 | | |
| | 0.0198 | 9.0 | 693 | 0.0017 | | |
| | 0.0086 | 10.0 | 770 | 0.0041 | | |
| ### Framework versions | |
| - Transformers 5.16.1 | |
| - Pytorch 2.11.0+cu128 | |
| - Datasets 4.8.5 | |
| - Tokenizers 0.23.1 | |