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
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: 0.10869565217391304
swinv2-tiny-patch4-window8-256-DMAE-15e
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: 7.9238
- 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-05
- 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
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.86 | 3 | 7.9238 | 0.1087 |
| No log | 2.0 | 7 | 7.8746 | 0.1087 |
| 7.9273 | 2.86 | 10 | 7.8185 | 0.1087 |
| 7.9273 | 4.0 | 14 | 7.6996 | 0.1087 |
| 7.9273 | 4.86 | 17 | 7.5876 | 0.1087 |
| 7.6529 | 6.0 | 21 | 7.4189 | 0.1087 |
| 7.6529 | 6.86 | 24 | 7.2678 | 0.1087 |
| 7.6529 | 8.0 | 28 | 7.0489 | 0.1087 |
| 7.1057 | 8.86 | 31 | 6.8846 | 0.1087 |
| 7.1057 | 10.0 | 35 | 6.6868 | 0.1087 |
| 7.1057 | 10.86 | 38 | 6.5595 | 0.1087 |
| 6.8483 | 12.0 | 42 | 6.3826 | 0.1087 |
| 6.8483 | 12.86 | 45 | 6.2276 | 0.1087 |
| 6.8483 | 14.0 | 49 | 6.0366 | 0.1087 |
| 6.224 | 14.86 | 52 | 5.9044 | 0.1087 |
| 6.224 | 16.0 | 56 | 5.7383 | 0.1087 |
| 6.224 | 16.86 | 59 | 5.6266 | 0.1087 |
| 5.8234 | 18.0 | 63 | 5.4871 | 0.1087 |
| 5.8234 | 18.86 | 66 | 5.3891 | 0.1087 |
| 5.5423 | 20.0 | 70 | 5.2672 | 0.1087 |
| 5.5423 | 20.86 | 73 | 5.1809 | 0.1087 |
| 5.5423 | 22.0 | 77 | 5.0741 | 0.1087 |
| 5.2547 | 22.86 | 80 | 5.0007 | 0.1087 |
| 5.2547 | 24.0 | 84 | 4.9116 | 0.1087 |
| 5.2547 | 24.86 | 87 | 4.8505 | 0.1087 |
| 5.0166 | 26.0 | 91 | 4.7770 | 0.1087 |
| 5.0166 | 26.86 | 94 | 4.7281 | 0.1087 |
| 5.0166 | 28.0 | 98 | 4.6712 | 0.1087 |
| 4.7751 | 28.86 | 101 | 4.6348 | 0.1087 |
| 4.7751 | 30.0 | 105 | 4.5943 | 0.1087 |
| 4.7751 | 30.86 | 108 | 4.5701 | 0.1087 |
| 4.7321 | 32.0 | 112 | 4.5458 | 0.1087 |
| 4.7321 | 32.86 | 115 | 4.5336 | 0.1087 |
| 4.7321 | 34.0 | 119 | 4.5255 | 0.1087 |
| 4.6731 | 34.29 | 120 | 4.5249 | 0.1087 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0