metadata
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-DMAE-15e-6
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: 0.10869565217391304
swinv2-tiny-patch4-window8-256-DMAE-15e-6
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 7.9433
- Accuracy: 0.1087
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: 1.5e-06
- 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 | 0.86 | 3 | 7.9433 | 0.1087 |
| No log | 2.0 | 7 | 7.9425 | 0.1087 |
| 7.9661 | 2.86 | 10 | 7.9414 | 0.1087 |
| 7.9661 | 4.0 | 14 | 7.9392 | 0.1087 |
| 7.9661 | 4.86 | 17 | 7.9372 | 0.1087 |
| 7.9068 | 6.0 | 21 | 7.9343 | 0.1087 |
| 7.9068 | 6.86 | 24 | 7.9320 | 0.1087 |
| 7.9068 | 8.0 | 28 | 7.9288 | 0.1087 |
| 7.7849 | 8.86 | 31 | 7.9262 | 0.1087 |
| 7.7849 | 10.0 | 35 | 7.9226 | 0.1087 |
| 7.7849 | 10.86 | 38 | 7.9198 | 0.1087 |
| 8.0613 | 12.0 | 42 | 7.9159 | 0.1087 |
| 8.0613 | 12.86 | 45 | 7.9129 | 0.1087 |
| 8.0613 | 14.0 | 49 | 7.9088 | 0.1087 |
| 7.827 | 14.86 | 52 | 7.9057 | 0.1087 |
| 7.827 | 16.0 | 56 | 7.9015 | 0.1087 |
| 7.827 | 16.86 | 59 | 7.8984 | 0.1087 |
| 7.8359 | 18.0 | 63 | 7.8942 | 0.1087 |
| 7.8359 | 18.86 | 66 | 7.8910 | 0.1087 |
| 7.9032 | 20.0 | 70 | 7.8869 | 0.1087 |
| 7.9032 | 20.86 | 73 | 7.8839 | 0.1087 |
| 7.9032 | 22.0 | 77 | 7.8799 | 0.1087 |
| 7.9294 | 22.86 | 80 | 7.8771 | 0.1087 |
| 7.9294 | 24.0 | 84 | 7.8735 | 0.1087 |
| 7.9294 | 24.86 | 87 | 7.8710 | 0.1087 |
| 7.9185 | 26.0 | 91 | 7.8679 | 0.1087 |
| 7.9185 | 26.86 | 94 | 7.8659 | 0.1087 |
| 7.9185 | 28.0 | 98 | 7.8634 | 0.1087 |
| 7.7948 | 28.86 | 101 | 7.8618 | 0.1087 |
| 7.7948 | 30.0 | 105 | 7.8600 | 0.1087 |
| 7.7948 | 30.86 | 108 | 7.8589 | 0.1087 |
| 7.906 | 32.0 | 112 | 7.8578 | 0.1087 |
| 7.906 | 32.86 | 115 | 7.8573 | 0.1087 |
| 7.906 | 34.0 | 119 | 7.8569 | 0.1087 |
| 7.9031 | 34.29 | 120 | 7.8569 | 0.1087 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0