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-ucf101-subset
  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-ucf101-subset

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: 1.9424
- Accuracy: 0.5333

## 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: 3
- eval_batch_size: 3
- 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: 800

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5319        | 0.1   | 80   | 0.9986          | 0.5      |
| 0.5747        | 1.1   | 160  | 1.1108          | 0.5167   |
| 0.4534        | 2.1   | 240  | 1.3027          | 0.4833   |
| 0.2058        | 3.1   | 320  | 2.2119          | 0.5      |
| 0.504         | 4.1   | 400  | 2.3382          | 0.5167   |
| 0.377         | 5.1   | 480  | 1.6407          | 0.65     |
| 0.8547        | 6.1   | 560  | 2.3820          | 0.5      |
| 0.4046        | 7.1   | 640  | 1.9771          | 0.5333   |
| 0.006         | 8.1   | 720  | 2.0629          | 0.55     |
| 0.0123        | 9.1   | 800  | 1.9424          | 0.5333   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
