|
--- |
|
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-finetuned-microbes |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: imagefolder |
|
type: imagefolder |
|
config: default |
|
split: train |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.7051282051282052 |
|
--- |
|
|
|
<!-- 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-finetuned-microbes |
|
|
|
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: 1.0939 |
|
- Accuracy: 0.7051 |
|
|
|
## 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: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 4.1153 | 0.97 | 16 | 3.8048 | 0.1239 | |
|
| 3.2047 | 2.0 | 33 | 2.9123 | 0.2949 | |
|
| 2.6979 | 2.97 | 49 | 2.2162 | 0.4231 | |
|
| 1.9422 | 4.0 | 66 | 1.8476 | 0.5043 | |
|
| 1.5677 | 4.97 | 82 | 1.6194 | 0.5684 | |
|
| 1.3485 | 6.0 | 99 | 1.4825 | 0.5855 | |
|
| 1.146 | 6.97 | 115 | 1.4073 | 0.5983 | |
|
| 1.0408 | 8.0 | 132 | 1.2730 | 0.6325 | |
|
| 0.9334 | 8.97 | 148 | 1.2782 | 0.6282 | |
|
| 0.8702 | 10.0 | 165 | 1.1758 | 0.6752 | |
|
| 0.8589 | 10.97 | 181 | 1.1652 | 0.6838 | |
|
| 0.7607 | 12.0 | 198 | 1.2129 | 0.6795 | |
|
| 0.7676 | 12.97 | 214 | 1.1509 | 0.6795 | |
|
| 0.7359 | 14.0 | 231 | 1.1327 | 0.6966 | |
|
| 0.7491 | 14.97 | 247 | 1.1059 | 0.6966 | |
|
| 0.6664 | 16.0 | 264 | 1.1413 | 0.6923 | |
|
| 0.618 | 16.97 | 280 | 1.0954 | 0.7009 | |
|
| 0.6504 | 18.0 | 297 | 1.1030 | 0.7009 | |
|
| 0.6241 | 18.97 | 313 | 1.0956 | 0.7009 | |
|
| 0.6258 | 19.39 | 320 | 1.0939 | 0.7051 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.32.1 |
|
- Pytorch 2.0.1+cpu |
|
- Datasets 2.14.4 |
|
- Tokenizers 0.13.3 |
|
|
