transformers tensorboard safetensors videomae video-classification generated_from_trainer base_model:MCG-NJU/videomae-base license:cc-by-nc-4.0 endpoints_compatible region:us
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-emonet-subset_5epochs
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# videomae-base-finetuned-emonet-subset_5epochs

This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8968
- Accuracy: 0.3178

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5850

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7451        | 0.1   | 586  | 1.8405          | 0.3507   |
| 1.7772        | 1.1   | 1172 | 2.0249          | 0.3492   |
| 1.5554        | 2.1   | 1758 | 2.1923          | 0.3522   |
| 1.4067        | 3.1   | 2344 | 2.2090          | 0.2660   |
| 1.2998        | 4.1   | 2930 | 2.1675          | 0.3626   |
| 1.3817        | 5.1   | 3516 | 2.2724          | 0.0817   |
| 1.2132        | 6.1   | 4102 | 2.1633          | 0.1129   |
| 1.0237        | 7.1   | 4688 | 2.0873          | 0.2645   |
| 1.1383        | 8.1   | 5274 | 2.0901          | 0.2140   |
| 1.0591        | 9.1   | 5850 | 2.0897          | 0.2125   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
