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-ve-Ub
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: 0.09803921568627451
swinv2-tiny-patch4-window8-256-ve-Ub
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: 8.0201
- Accuracy: 0.0980
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: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.57 | 1 | 8.0201 | 0.0980 |
| No log | 1.71 | 3 | 8.0044 | 0.0980 |
| No log | 2.86 | 5 | 7.9306 | 0.0980 |
| No log | 4.0 | 7 | 7.7713 | 0.0980 |
| No log | 4.57 | 8 | 7.6511 | 0.0980 |
| 7.7785 | 5.71 | 10 | 7.3653 | 0.0980 |
| 7.7785 | 6.86 | 12 | 7.0246 | 0.0980 |
| 7.7785 | 8.0 | 14 | 6.6413 | 0.0980 |
| 7.7785 | 8.57 | 15 | 6.4670 | 0.0980 |
| 7.7785 | 9.71 | 17 | 6.1321 | 0.0980 |
| 7.7785 | 10.86 | 19 | 5.8360 | 0.0980 |
| 6.5357 | 12.0 | 21 | 5.5743 | 0.0980 |
| 6.5357 | 12.57 | 22 | 5.4552 | 0.0980 |
| 6.5357 | 13.71 | 24 | 5.2367 | 0.0980 |
| 6.5357 | 14.86 | 26 | 5.0418 | 0.0980 |
| 6.5357 | 16.0 | 28 | 4.8706 | 0.0980 |
| 6.5357 | 16.57 | 29 | 4.7939 | 0.0980 |
| 5.2494 | 17.71 | 31 | 4.6596 | 0.0980 |
| 5.2494 | 18.86 | 33 | 4.5508 | 0.0980 |
| 5.2494 | 20.0 | 35 | 4.4676 | 0.0980 |
| 5.2494 | 20.57 | 36 | 4.4356 | 0.0980 |
| 5.2494 | 21.71 | 38 | 4.3906 | 0.0980 |
| 4.5614 | 22.86 | 40 | 4.3714 | 0.0980 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0