---
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: dinov2-base-ODIR-5K
  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: 0.7188755020080321
    - name: F1
      type: f1
      value: 0.6332945285215367
---

<!-- 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-ODIR-5K

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: 0.5700
- Accuracy: 0.7189
- F1: 0.6333

## 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: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.6374        | 0.9858 | 52   | 0.6186          | 0.6778   | 0.2031 |
| 0.5789        | 1.9905 | 105  | 0.5661          | 0.7153   | 0.3794 |
| 0.5368        | 2.9953 | 158  | 0.5334          | 0.7407   | 0.5756 |
| 0.4162        | 4.0    | 211  | 0.5747          | 0.6983   | 0.6198 |
| 0.3679        | 4.9858 | 263  | 0.5700          | 0.7189   | 0.6333 |
| 0.2431        | 5.9147 | 312  | 0.6111          | 0.7564   | 0.6331 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1