Instructions to use AFZAL0008/SwimV3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AFZAL0008/SwimV3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AFZAL0008/SwimV3") 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("AFZAL0008/SwimV3") model = AutoModelForImageClassification.from_pretrained("AFZAL0008/SwimV3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
SwimV3
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5802
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 |
|---|---|---|---|
| 0.7510 | 1.0 | 77 | 0.6857 |
| 0.6778 | 2.0 | 154 | 0.7155 |
| 0.6506 | 3.0 | 231 | 1.0518 |
| 0.6437 | 4.0 | 308 | 0.6993 |
| 0.6104 | 5.0 | 385 | 0.7562 |
| 0.5939 | 6.0 | 462 | 0.6289 |
| 0.5720 | 7.0 | 539 | 0.6105 |
| 0.5617 | 8.0 | 616 | 0.5665 |
| 0.5571 | 9.0 | 693 | 0.6100 |
| 0.5227 | 10.0 | 770 | 0.5802 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.23.1
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Model tree for AFZAL0008/SwimV3
Base model
microsoft/swinv2-tiny-patch4-window8-256