---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: dinov2-finetuned-har
  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: 0.9148148148148149
---

<!-- 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-finetuned-har

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: 0.3078
- Accuracy: 0.9148

## 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.9429        | 0.9910 | 83   | 0.5624          | 0.8328   |
| 0.7912        | 1.9940 | 167  | 0.4755          | 0.8587   |
| 0.7371        | 2.9970 | 251  | 0.4584          | 0.8550   |
| 0.5915        | 4.0    | 335  | 0.3870          | 0.8762   |
| 0.5635        | 4.9910 | 418  | 0.4037          | 0.8704   |
| 0.498         | 5.9940 | 502  | 0.3876          | 0.8804   |
| 0.4541        | 6.9970 | 586  | 0.3612          | 0.8884   |
| 0.3513        | 8.0    | 670  | 0.3240          | 0.9053   |
| 0.2963        | 8.9910 | 753  | 0.3176          | 0.9116   |
| 0.2815        | 9.9104 | 830  | 0.3078          | 0.9148   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1