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---
language:
- eng
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dinov2-large-2024_01_15-with_data_aug_batch-size32_epochs20_freeze
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. -->
# dinov2-large-2024_01_15-with_data_aug_batch-size32_epochs20_freeze
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the multilabel_complete_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0891
- F1 Micro: 0.8422
- F1 Macro: 0.7067
- Roc Auc: 0.8958
- Accuracy: 0.5445
- Learning Rate: 0.0001
## 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: 0.01
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 Macro | F1 Micro | Validation Loss | Roc Auc | Rate |
|:-------------:|:-----:|:----:|:--------:|:--------:|:--------:|:---------------:|:-------:|:------:|
| No log | 1.0 | 274 | 0.4728 | 0.5944 | 0.7681 | 0.1364 | 0.8536 | 0.001 |
| 0.2417 | 2.0 | 548 | 0.5028 | 0.6565 | 0.8040 | 0.1118 | 0.8701 | 0.001 |
| 0.2417 | 3.0 | 822 | 0.5122 | 0.6697 | 0.8124 | 0.1061 | 0.8763 | 0.001 |
| 0.1314 | 4.0 | 1096 | 0.5003 | 0.6643 | 0.8168 | 0.1062 | 0.8832 | 0.001 |
| 0.1314 | 5.0 | 1370 | 0.5178 | 0.6736 | 0.8176 | 0.1032 | 0.8783 | 0.001 |
| 0.1235 | 6.0 | 1644 | 0.5335 | 0.6928 | 0.8255 | 0.1027 | 0.8906 | 0.001 |
| 0.1235 | 7.0 | 1918 | 0.5237 | 0.6767 | 0.8205 | 0.1027 | 0.8774 | 0.001 |
| 0.1196 | 8.0 | 2192 | 0.5181 | 0.6758 | 0.8176 | 0.1027 | 0.8775 | 0.001 |
| 0.1196 | 9.0 | 2466 | 0.5335 | 0.6807 | 0.8224 | 0.0994 | 0.8765 | 0.001 |
| 0.117 | 10.0 | 2740 | 0.5167 | 0.6870 | 0.8283 | 0.1007 | 0.8937 | 0.001 |
| 0.1163 | 11.0 | 3014 | 0.5195 | 0.6925 | 0.8298 | 0.0971 | 0.8898 | 0.001 |
| 0.1163 | 12.0 | 3288 | 0.5230 | 0.7006 | 0.8282 | 0.0987 | 0.8861 | 0.001 |
| 0.1156 | 13.0 | 3562 | 0.5342 | 0.7065 | 0.8275 | 0.1017 | 0.8903 | 0.001 |
| 0.1156 | 14.0 | 3836 | 0.5276 | 0.6968 | 0.8243 | 0.1224 | 0.8851 | 0.001 |
| 0.1137 | 15.0 | 4110 | 0.5300 | 0.6958 | 0.8295 | 0.0981 | 0.8904 | 0.001 |
| 0.1137 | 16.0 | 4384 | 0.5398 | 0.7179 | 0.8412 | 0.0919 | 0.8981 | 0.0001 |
| 0.1091 | 17.0 | 4658 | 0.5433 | 0.7235 | 0.8431 | 0.0924 | 0.8997 | 0.0001 |
| 0.1091 | 18.0 | 4932 | 0.5447 | 0.7166 | 0.8448 | 0.0904 | 0.8984 | 0.0001 |
| 0.1026 | 19.0 | 5206 | 0.0903 | 0.8448 | 0.7248 | 0.8963 | 0.5443 | 0.0001 |
| 0.1026 | 20.0 | 5480 | 0.0887 | 0.8439 | 0.7176 | 0.8971 | 0.5422 | 0.0001 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.15.0
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