|
|
---
|
|
|
license: apache-2.0
|
|
|
base_model: microsoft/swinv2-tiny-patch4-window8-256
|
|
|
tags:
|
|
|
- generated_from_trainer
|
|
|
datasets:
|
|
|
- imagefolder
|
|
|
metrics:
|
|
|
- accuracy
|
|
|
model-index:
|
|
|
- name: swinv2-tiny-patch4-window8-256-OT
|
|
|
results:
|
|
|
- task:
|
|
|
name: Image Classification
|
|
|
type: image-classification
|
|
|
dataset:
|
|
|
name: imagefolder
|
|
|
type: imagefolder
|
|
|
config: default
|
|
|
split: validation
|
|
|
args: default
|
|
|
metrics:
|
|
|
- name: Accuracy
|
|
|
type: accuracy
|
|
|
value: 0.8225806451612904
|
|
|
---
|
|
|
|
|
|
<!-- 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-OT
|
|
|
|
|
|
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
|
|
|
It achieves the following results on the evaluation set:
|
|
|
- Loss: 0.6192
|
|
|
- Accuracy: 0.8226
|
|
|
|
|
|
## 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: 0.00015
|
|
|
- train_batch_size: 16
|
|
|
- eval_batch_size: 16
|
|
|
- seed: 42
|
|
|
- gradient_accumulation_steps: 4
|
|
|
- total_train_batch_size: 64
|
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
|
|
- lr_scheduler_type: linear
|
|
|
- lr_scheduler_warmup_ratio: 0.1
|
|
|
- num_epochs: 40
|
|
|
|
|
|
### Training results
|
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
|
|
| No log | 0.91 | 5 | 8.8439 | 0.0806 |
|
|
|
| 8.7922 | 2.0 | 11 | 8.0016 | 0.0806 |
|
|
|
| 8.7922 | 2.91 | 16 | 6.0009 | 0.0806 |
|
|
|
| 6.5264 | 4.0 | 22 | 2.7431 | 0.0806 |
|
|
|
| 6.5264 | 4.91 | 27 | 1.3018 | 0.4516 |
|
|
|
| 2.16 | 6.0 | 33 | 1.2696 | 0.4516 |
|
|
|
| 2.16 | 6.91 | 38 | 1.2057 | 0.4516 |
|
|
|
| 1.2876 | 8.0 | 44 | 1.2157 | 0.4516 |
|
|
|
| 1.2876 | 8.91 | 49 | 1.2459 | 0.4516 |
|
|
|
| 1.2456 | 10.0 | 55 | 1.2110 | 0.4516 |
|
|
|
| 1.1901 | 10.91 | 60 | 1.1861 | 0.4516 |
|
|
|
| 1.1901 | 12.0 | 66 | 1.0847 | 0.4677 |
|
|
|
| 1.0665 | 12.91 | 71 | 1.0944 | 0.4677 |
|
|
|
| 1.0665 | 14.0 | 77 | 1.1854 | 0.4677 |
|
|
|
| 1.033 | 14.91 | 82 | 1.0252 | 0.5 |
|
|
|
| 1.033 | 16.0 | 88 | 1.2164 | 0.5161 |
|
|
|
| 1.0323 | 16.91 | 93 | 1.0643 | 0.5 |
|
|
|
| 1.0323 | 18.0 | 99 | 0.9802 | 0.6613 |
|
|
|
| 0.9329 | 18.91 | 104 | 0.9475 | 0.5968 |
|
|
|
| 0.8619 | 20.0 | 110 | 0.9115 | 0.6452 |
|
|
|
| 0.8619 | 20.91 | 115 | 0.8894 | 0.6452 |
|
|
|
| 0.8019 | 22.0 | 121 | 0.8276 | 0.6935 |
|
|
|
| 0.8019 | 22.91 | 126 | 0.8156 | 0.6774 |
|
|
|
| 0.7675 | 24.0 | 132 | 0.7928 | 0.6290 |
|
|
|
| 0.7675 | 24.91 | 137 | 0.7163 | 0.7419 |
|
|
|
| 0.6762 | 26.0 | 143 | 0.7388 | 0.6774 |
|
|
|
| 0.6762 | 26.91 | 148 | 0.6519 | 0.7581 |
|
|
|
| 0.6771 | 28.0 | 154 | 0.6710 | 0.7419 |
|
|
|
| 0.6771 | 28.91 | 159 | 0.6074 | 0.7581 |
|
|
|
| 0.6424 | 30.0 | 165 | 0.6729 | 0.7258 |
|
|
|
| 0.6139 | 30.91 | 170 | 0.5744 | 0.7903 |
|
|
|
| 0.6139 | 32.0 | 176 | 0.6192 | 0.8226 |
|
|
|
| 0.5713 | 32.91 | 181 | 0.6453 | 0.7903 |
|
|
|
| 0.5713 | 34.0 | 187 | 0.6392 | 0.7903 |
|
|
|
| 0.5462 | 34.91 | 192 | 0.5956 | 0.8226 |
|
|
|
| 0.5462 | 36.0 | 198 | 0.5893 | 0.8226 |
|
|
|
| 0.5393 | 36.36 | 200 | 0.5898 | 0.8226 |
|
|
|
|
|
|
|
|
|
### Framework versions
|
|
|
|
|
|
- Transformers 4.36.2
|
|
|
- Pytorch 2.1.2+cu118
|
|
|
- Datasets 2.16.1
|
|
|
- Tokenizers 0.15.0
|
|
|
|
