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hiera-finetuned-stroke-multi
BTX24/hiera-finetuned-stroke-multi
hiera-finetuned-stroke-multi is a image classification model from BTX24. Use it when you need a label for an image. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- hiera
- Access
- Public
- Created
- Jun 9, 2025
- Updated
- Jun 9, 2025
- SHA
- 4aa7e67b
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
hiera-finetuned-stroke-multi
This model is a fine-tuned version of BTX24/hiera-base-224-in1k-hf-finetuned-stroke-binary on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0648
- Accuracy: 0.9861
- F1: 0.9861
- Precision: 0.9862
- Recall: 0.9861
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0612 | 1.0997 | 100 | 0.1259 | 0.9639 | 0.9635 | 0.9647 | 0.9639 |
| 0.0694 | 2.1994 | 200 | 0.0735 | 0.9778 | 0.9777 | 0.9780 | 0.9778 |
| 0.0632 | 3.2992 | 300 | 0.0867 | 0.9764 | 0.9763 | 0.9765 | 0.9764 |
| 0.064 | 4.3989 | 400 | 0.0677 | 0.9827 | 0.9826 | 0.9826 | 0.9827 |
| 0.0614 | 5.4986 | 500 | 0.0766 | 0.9827 | 0.9827 | 0.9830 | 0.9827 |
| 0.0361 | 6.5983 | 600 | 0.1001 | 0.9764 | 0.9763 | 0.9764 | 0.9764 |
| 0.0321 | 7.6981 | 700 | 0.0701 | 0.9827 | 0.9827 | 0.9828 | 0.9827 |
| 0.0495 | 8.7978 | 800 | 0.0648 | 0.9861 | 0.9861 | 0.9862 | 0.9861 |
| 0.0339 | 9.8975 | 900 | 0.0615 | 0.9854 | 0.9854 | 0.9855 | 0.9854 |
| 0.0384 | 10.9972 | 1000 | 0.0628 | 0.9854 | 0.9854 | 0.9855 | 0.9854 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Image Classification
- Library
- transformers
- Type
- hiera
- License
- cc-by-nc-4.0
- Created
- Jun 9, 2025
- Updated
- Jun 9, 2025