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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV38
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV38
swinv2-tiny-patch4-window8-256-dmae-humeda-DAV38 is a image classification model from RobertoSonic. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- swinv2
- Access
- Public
- Created
- Feb 4, 2025
- Updated
- Feb 4, 2025
- SHA
- e3d09d1f
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. -->
swinv2-tiny-patch4-window8-256-dmae-humeda-DAV38
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1368
- Accuracy: 0.6591
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 1.4929 | 0.4545 |
| No log | 2.0 | 8 | 1.2769 | 0.4545 |
| 4.8085 | 3.0 | 12 | 1.2009 | 0.5682 |
| 4.8085 | 4.0 | 16 | 1.0885 | 0.5114 |
| 4.8085 | 5.0 | 20 | 0.9977 | 0.6591 |
| 3.4418 | 6.0 | 24 | 0.9279 | 0.7045 |
| 3.4418 | 7.0 | 28 | 0.9311 | 0.6591 |
| 3.4418 | 8.0 | 32 | 0.9485 | 0.6591 |
| 2.4374 | 9.0 | 36 | 0.9279 | 0.6705 |
| 2.4374 | 10.0 | 40 | 0.9567 | 0.6705 |
| 2.4374 | 11.0 | 44 | 0.9832 | 0.6705 |
| 1.6575 | 12.0 | 48 | 0.9993 | 0.6705 |
| 1.6575 | 13.0 | 52 | 1.0360 | 0.6705 |
| 1.6575 | 14.0 | 56 | 1.0418 | 0.6591 |
| 1.1852 | 15.0 | 60 | 1.0619 | 0.6477 |
| 1.1852 | 16.0 | 64 | 1.0820 | 0.6705 |
| 1.1852 | 17.0 | 68 | 1.1155 | 0.6477 |
| 0.9213 | 18.0 | 72 | 1.1154 | 0.6591 |
| 0.9213 | 19.0 | 76 | 1.1240 | 0.6591 |
| 0.9213 | 20.0 | 80 | 1.1349 | 0.6591 |
| 0.7877 | 21.0 | 84 | 1.1367 | 0.6591 |
| 0.7877 | 22.0 | 88 | 1.1367 | 0.6591 |
| 0.7877 | 22.6154 | 90 | 1.1368 | 0.6591 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- swinv2
- License
- apache-2.0
- Created
- Feb 4, 2025
- Updated
- Feb 4, 2025