---
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: ALL_RGBCROP_ori16F-8B16F-GACWD5lrDO
  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. -->

# ALL_RGBCROP_ori16F-8B16F-GACWD5lrDO

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: 0.4092
- Accuracy: 0.8144

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1920

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.694         | 0.025  | 48   | 0.7127          | 0.4939   |
| 0.6395        | 1.025  | 96   | 0.6772          | 0.5976   |
| 0.4871        | 2.025  | 144  | 0.6142          | 0.6707   |
| 0.4162        | 3.025  | 192  | 0.5418          | 0.7256   |
| 0.2336        | 4.025  | 240  | 0.5030          | 0.7622   |
| 0.159         | 5.025  | 288  | 0.5045          | 0.7927   |
| 0.1486        | 6.025  | 336  | 0.5186          | 0.7805   |
| 0.0997        | 7.025  | 384  | 0.5649          | 0.7866   |
| 0.07          | 8.025  | 432  | 0.6180          | 0.7805   |
| 0.0377        | 9.025  | 480  | 0.6364          | 0.7927   |
| 0.0103        | 10.025 | 528  | 0.7102          | 0.7866   |


### Framework versions

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