metadata
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ky-finetuned-skindiseaseicthuawei32
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: 0.9622508792497069
ky-finetuned-skindiseaseicthuawei32
This model is a fine-tuned version of facebook/dinov2-base on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1058
- Accuracy: 0.9623
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3894 | 1.0 | 300 | 0.6160 | 0.8061 |
| 0.6543 | 2.0 | 600 | 0.4378 | 0.8635 |
| 0.471 | 3.0 | 900 | 0.2566 | 0.9161 |
| 0.3853 | 4.0 | 1200 | 0.2498 | 0.9135 |
| 0.3225 | 5.0 | 1500 | 0.2157 | 0.9290 |
| 0.2769 | 6.0 | 1800 | 0.1747 | 0.9407 |
| 0.2364 | 7.0 | 2100 | 0.1502 | 0.9487 |
| 0.2005 | 8.0 | 2400 | 0.1282 | 0.9547 |
| 0.1737 | 9.0 | 2700 | 0.1129 | 0.9597 |
| 0.1468 | 10.0 | 3000 | 0.1058 | 0.9623 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0