|
--- |
|
license: apache-2.0 |
|
base_model: microsoft/swin-tiny-patch4-window7-224 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: imagefolder |
|
type: imagefolder |
|
config: default |
|
split: test |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.5874543393374481 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough |
|
|
|
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.9991 |
|
- Accuracy: 0.5875 |
|
|
|
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
| 1.5983 | 1.0 | 1240 | 1.3399 | 0.4436 | |
|
| 1.5587 | 2.0 | 2481 | 1.3298 | 0.4366 | |
|
| 1.4955 | 3.0 | 3721 | 1.1679 | 0.5129 | |
|
| 1.4884 | 4.0 | 4962 | 1.1331 | 0.5299 | |
|
| 1.4413 | 5.0 | 6202 | 1.1286 | 0.5287 | |
|
| 1.4395 | 6.0 | 7443 | 1.1316 | 0.5226 | |
|
| 1.4497 | 7.0 | 8683 | 1.2127 | 0.4844 | |
|
| 1.3988 | 8.0 | 9924 | 1.1119 | 0.5301 | |
|
| 1.4457 | 9.0 | 11164 | 1.0984 | 0.5389 | |
|
| 1.4153 | 10.0 | 12405 | 1.1226 | 0.5269 | |
|
| 1.3962 | 11.0 | 13645 | 1.0610 | 0.5573 | |
|
| 1.3911 | 12.0 | 14886 | 1.0540 | 0.5595 | |
|
| 1.3617 | 13.0 | 16126 | 1.0646 | 0.5530 | |
|
| 1.3766 | 14.0 | 17367 | 1.0722 | 0.5532 | |
|
| 1.3693 | 15.0 | 18607 | 1.0243 | 0.5721 | |
|
| 1.3624 | 16.0 | 19848 | 1.0212 | 0.5763 | |
|
| 1.3638 | 17.0 | 21088 | 1.0667 | 0.5580 | |
|
| 1.4007 | 18.0 | 22329 | 1.0314 | 0.5730 | |
|
| 1.3415 | 19.0 | 23569 | 1.0191 | 0.5755 | |
|
| 1.3802 | 20.0 | 24810 | 1.0142 | 0.5770 | |
|
| 1.3572 | 21.0 | 26050 | 1.0125 | 0.5771 | |
|
| 1.2962 | 22.0 | 27291 | 1.0167 | 0.5763 | |
|
| 1.2831 | 23.0 | 28531 | 1.0043 | 0.5829 | |
|
| 1.3272 | 24.0 | 29772 | 0.9990 | 0.5858 | |
|
| 1.3197 | 25.0 | 31012 | 1.0033 | 0.5830 | |
|
| 1.3203 | 26.0 | 32253 | 1.0075 | 0.5818 | |
|
| 1.3172 | 27.0 | 33493 | 1.0008 | 0.5852 | |
|
| 1.3197 | 28.0 | 34734 | 1.0016 | 0.5847 | |
|
| 1.2879 | 29.0 | 35974 | 1.0017 | 0.5867 | |
|
| 1.2907 | 29.99 | 37200 | 0.9991 | 0.5875 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.2 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.17.0 |
|
- Tokenizers 0.15.2 |
|
|
