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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV39
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV39
swinv2-tiny-patch4-window8-256-dmae-humeda-DAV39 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
- fe50eb6d
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-DAV39
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: 0.9091
- Accuracy: 0.75
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: 0.0001
- 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 | 1.3890 | 0.4318 |
| No log | 2.0 | 8 | 1.2016 | 0.5682 |
| 4.424 | 3.0 | 12 | 1.0279 | 0.6364 |
| 4.424 | 4.0 | 16 | 0.9227 | 0.6477 |
| 4.424 | 5.0 | 20 | 0.9294 | 0.6818 |
| 2.6292 | 6.0 | 24 | 0.9936 | 0.625 |
| 2.6292 | 7.0 | 28 | 0.9816 | 0.6705 |
| 2.6292 | 8.0 | 32 | 0.9091 | 0.75 |
| 1.5117 | 9.0 | 36 | 0.8995 | 0.7159 |
| 1.5117 | 10.0 | 40 | 1.0646 | 0.7045 |
| 1.5117 | 11.0 | 44 | 1.1826 | 0.6705 |
| 0.7053 | 12.0 | 48 | 1.2360 | 0.6932 |
| 0.7053 | 13.0 | 52 | 1.4215 | 0.6818 |
| 0.7053 | 14.0 | 56 | 1.3694 | 0.7045 |
| 0.2599 | 15.0 | 60 | 1.2998 | 0.7159 |
| 0.2599 | 16.0 | 64 | 1.3703 | 0.7159 |
| 0.2599 | 17.0 | 68 | 1.4626 | 0.7159 |
| 0.1282 | 18.0 | 72 | 1.5181 | 0.7159 |
| 0.1282 | 19.0 | 76 | 1.5692 | 0.7045 |
| 0.1282 | 20.0 | 80 | 1.5965 | 0.6932 |
| 0.0816 | 21.0 | 84 | 1.6018 | 0.7045 |
| 0.0816 | 22.0 | 88 | 1.6046 | 0.7045 |
| 0.0816 | 22.6154 | 90 | 1.6049 | 0.7045 |
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