transformers pytorch 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
  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

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: 0.0757
- Accuracy: 0.9837

## 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: 6
- eval_batch_size: 6
- 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: 8320

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1009        | 0.1   | 833  | 1.2521          | 0.6558   |
| 0.2821        | 1.1   | 1666 | 0.4310          | 0.8623   |
| 0.4519        | 2.1   | 2499 | 0.2645          | 0.9223   |
| 0.137         | 3.1   | 3332 | 0.2231          | 0.9371   |
| 0.1014        | 4.1   | 4165 | 0.1516          | 0.9593   |
| 0.0084        | 5.1   | 4998 | 0.1478          | 0.9682   |
| 0.0041        | 6.1   | 5831 | 0.1284          | 0.9652   |
| 0.006         | 7.1   | 6664 | 0.1214          | 0.9726   |
| 0.001         | 8.1   | 7497 | 0.0745          | 0.9822   |
| 0.0005        | 9.1   | 8320 | 0.0757          | 0.9837   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.1+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2
