---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: RALL_RGBCROP_Aug16F-polynomial
  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. -->

# RALL_RGBCROP_Aug16F-polynomial

This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5478
- Accuracy: 0.8635

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3462

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4872        | 0.0835 | 289  | 0.5884          | 0.6667   |
| 0.2293        | 1.0835 | 578  | 0.5230          | 0.7975   |
| 0.0214        | 2.0835 | 867  | 0.7384          | 0.8016   |
| 0.0423        | 3.0835 | 1156 | 0.8912          | 0.8139   |
| 0.0008        | 4.0835 | 1445 | 1.0195          | 0.8016   |
| 0.0004        | 5.0835 | 1734 | 1.0783          | 0.7996   |
| 0.0003        | 6.0835 | 2023 | 1.1355          | 0.8016   |
| 0.0002        | 7.0835 | 2312 | 1.1726          | 0.7935   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1