---
library_name: transformers
license: other
base_model: facebook/dinov3-vitb16-pretrain-lvd1689m
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: outputs_dinov3_baseline
  results: []
---

<!-- 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. -->

# outputs_dinov3_baseline

This model is a fine-tuned version of [facebook/dinov3-vitb16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0569
- Precision: 0.9613
- Recall: 0.9664
- F1: 0.9639

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.4090        | 1.0   | 145  | 0.3883          | 0.5656    | 0.6565 | 0.6077 |
| 0.2000        | 2.0   | 290  | 0.2048          | 0.8247    | 0.8897 | 0.8560 |
| 0.1257        | 3.0   | 435  | 0.1353          | 0.9061    | 0.9422 | 0.9238 |
| 0.1012        | 4.0   | 580  | 0.1013          | 0.9413    | 0.9601 | 0.9506 |
| 0.0786        | 5.0   | 725  | 0.0808          | 0.9481    | 0.9590 | 0.9535 |
| 0.0676        | 6.0   | 870  | 0.0697          | 0.9545    | 0.9706 | 0.9625 |
| 0.0592        | 7.0   | 1015 | 0.0651          | 0.9553    | 0.9643 | 0.9597 |
| 0.0486        | 8.0   | 1160 | 0.0599          | 0.9572    | 0.9632 | 0.9602 |
| 0.0419        | 9.0   | 1305 | 0.0578          | 0.9634    | 0.9664 | 0.9649 |
| 0.0469        | 10.0  | 1450 | 0.0569          | 0.9613    | 0.9664 | 0.9639 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2