---
license: cc-by-nc-4.0
base_model: minhah/videomae-base-finetuned-crema-d8
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-crema-d8-finetuned-elder-creama-d-pretuned
  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. -->

# videomae-base-finetuned-crema-d8-finetuned-elder-creama-d-pretuned

This model is a fine-tuned version of [minhah/videomae-base-finetuned-crema-d8](https://huggingface.co/minhah/videomae-base-finetuned-crema-d8) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6702
- Accuracy: 0.3389

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1440

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6909        | 0.1   | 145  | 1.6484          | 0.3352   |
| 1.5944        | 1.1   | 290  | 1.6735          | 0.2798   |
| 1.5776        | 2.1   | 435  | 1.6654          | 0.3212   |
| 1.6768        | 3.1   | 580  | 1.7330          | 0.1858   |
| 1.6108        | 4.1   | 725  | 1.6919          | 0.2504   |
| 1.5103        | 5.1   | 870  | 1.6524          | 0.2805   |
| 1.5447        | 6.1   | 1015 | 1.6767          | 0.3086   |
| 1.5237        | 7.1   | 1160 | 1.7329          | 0.2553   |
| 1.4397        | 8.1   | 1305 | 1.7293          | 0.2475   |
| 1.4544        | 9.09  | 1440 | 1.7368          | 0.2518   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2