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---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: dinov2-base-finetuned-dermnet-lr3-5-0.05wd-csr
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: 0.7958001448225923
---
<!-- 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. -->
# dinov2-base-finetuned-dermnet-lr3-5-0.05wd-csr
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8119
- Accuracy: 0.7958
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.7488 | 1.0 | 98 | 2.5158 | 0.3722 |
| 1.9402 | 2.0 | 196 | 1.7710 | 0.5170 |
| 1.4938 | 3.0 | 294 | 1.4939 | 0.5996 |
| 1.1226 | 4.0 | 392 | 1.3168 | 0.6256 |
| 0.9329 | 5.0 | 490 | 1.1906 | 0.6705 |
| 0.8039 | 6.0 | 588 | 1.0882 | 0.7067 |
| 0.6426 | 7.0 | 686 | 1.1061 | 0.6930 |
| 0.5777 | 8.0 | 784 | 1.0133 | 0.7227 |
| 0.477 | 9.0 | 882 | 0.9681 | 0.7364 |
| 0.3961 | 10.0 | 980 | 0.9402 | 0.7581 |
| 0.3451 | 11.0 | 1078 | 0.9311 | 0.7509 |
| 0.337 | 12.0 | 1176 | 0.8897 | 0.7661 |
| 0.2348 | 13.0 | 1274 | 0.8616 | 0.7762 |
| 0.1992 | 14.0 | 1372 | 0.8241 | 0.7951 |
| 0.182 | 15.0 | 1470 | 0.8312 | 0.7878 |
| 0.1556 | 16.0 | 1568 | 0.8245 | 0.7857 |
| 0.1516 | 17.0 | 1666 | 0.8170 | 0.7958 |
| 0.1569 | 18.0 | 1764 | 0.8202 | 0.7878 |
| 0.1364 | 19.0 | 1862 | 0.8117 | 0.7951 |
| 0.1427 | 19.8021 | 1940 | 0.8119 | 0.7958 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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