| --- |
| license: mit |
| base_model: microsoft/git-base |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| model-index: |
| - name: git-base-captioning |
| 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. --> |
|
|
| # git-base-captioning |
|
|
| This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3575 |
| - Wer Score: 0.8322 |
|
|
| ## 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: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 8 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer Score | |
| |:-------------:|:------:|:----:|:---------------:|:---------:| |
| | 7.8992 | 0.3540 | 20 | 7.5466 | 11.1579 | |
| | 5.9879 | 0.7080 | 40 | 5.6121 | 5.1946 | |
| | 4.1288 | 1.0619 | 60 | 3.7153 | 4.5433 | |
| | 2.3477 | 1.4159 | 80 | 1.9989 | 4.0242 | |
| | 1.0242 | 1.7699 | 100 | 0.8650 | 0.8657 | |
| | 0.4954 | 2.1239 | 120 | 0.4766 | 0.8676 | |
| | 0.3365 | 2.4779 | 140 | 0.3993 | 3.7516 | |
| | 0.4286 | 2.8319 | 160 | 0.3773 | 0.8336 | |
| | 0.2952 | 3.1858 | 180 | 0.3663 | 0.8329 | |
| | 0.3996 | 3.5398 | 200 | 0.3619 | 0.8224 | |
| | 0.2629 | 3.8938 | 220 | 0.3574 | 0.8204 | |
| | 0.254 | 4.2478 | 240 | 0.3555 | 0.8178 | |
| | 0.2642 | 4.6018 | 260 | 0.3557 | 0.8132 | |
| | 0.2725 | 4.9558 | 280 | 0.3533 | 0.8139 | |
| | 0.2746 | 5.3097 | 300 | 0.3554 | 0.8093 | |
| | 0.1765 | 5.6637 | 320 | 0.3561 | 0.8244 | |
| | 0.2981 | 6.0177 | 340 | 0.3542 | 0.8375 | |
| | 0.1489 | 6.3717 | 360 | 0.3567 | 0.8329 | |
| | 0.256 | 6.7257 | 380 | 0.3553 | 0.8362 | |
| | 0.1574 | 7.0796 | 400 | 0.3558 | 0.8342 | |
| | 0.1836 | 7.4336 | 420 | 0.3566 | 0.8336 | |
| | 0.1697 | 7.7876 | 440 | 0.3578 | 0.8362 | |
| | 0.1596 | 8.1416 | 460 | 0.3571 | 0.8414 | |
| | 0.1628 | 8.4956 | 480 | 0.3579 | 0.8388 | |
| | 0.1958 | 8.8496 | 500 | 0.3572 | 0.8362 | |
| | 0.1695 | 9.2035 | 520 | 0.3575 | 0.8303 | |
| | 0.1686 | 9.5575 | 540 | 0.3576 | 0.8336 | |
| | 0.2166 | 9.9115 | 560 | 0.3575 | 0.8322 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
