|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: microsoft/swinv2-tiny-patch4-window8-256 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV41 |
|
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. --> |
|
|
|
# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV41 |
|
|
|
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.9385 |
|
- Accuracy: 0.6818 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 256 |
|
- 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: 40 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 1.0 | 2 | 1.4874 | 0.4318 | |
|
| No log | 2.0 | 4 | 1.3560 | 0.4432 | |
|
| No log | 3.0 | 6 | 1.3117 | 0.4432 | |
|
| No log | 4.0 | 8 | 1.2763 | 0.4432 | |
|
| No log | 5.0 | 10 | 1.2602 | 0.5682 | |
|
| 8.9996 | 6.0 | 12 | 1.2348 | 0.6023 | |
|
| 8.9996 | 7.0 | 14 | 1.1982 | 0.5795 | |
|
| 8.9996 | 8.0 | 16 | 1.1592 | 0.6136 | |
|
| 8.9996 | 9.0 | 18 | 1.1142 | 0.625 | |
|
| 8.9996 | 10.0 | 20 | 1.0682 | 0.6364 | |
|
| 8.9996 | 11.0 | 22 | 1.0256 | 0.6477 | |
|
| 7.429 | 12.0 | 24 | 0.9843 | 0.6705 | |
|
| 7.429 | 13.0 | 26 | 0.9602 | 0.6705 | |
|
| 7.429 | 14.0 | 28 | 0.9452 | 0.6591 | |
|
| 7.429 | 15.0 | 30 | 0.9385 | 0.6818 | |
|
| 7.429 | 16.0 | 32 | 0.9320 | 0.6705 | |
|
| 7.429 | 17.0 | 34 | 0.9285 | 0.6477 | |
|
| 6.2752 | 18.0 | 36 | 0.9239 | 0.6591 | |
|
| 6.2752 | 19.0 | 38 | 0.9214 | 0.6818 | |
|
| 6.2752 | 20.0 | 40 | 0.9206 | 0.6818 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.1 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
