---
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: videomae-base-finetuned-kinetics-allkisa-crop-background-0312-clip_duration-abnormal12_resize
  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. -->

# videomae-base-finetuned-kinetics-allkisa-crop-background-0312-clip_duration-abnormal12_resize

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: 0.3122
- Accuracy: 0.9393

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 0.05
- training_steps: 29600

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6616        | 0.01  | 296   | 0.3786          | 0.8675   |
| 0.1816        | 1.01  | 592   | 0.5763          | 0.8352   |
| 0.0266        | 2.01  | 888   | 0.6628          | 0.8078   |
| 0.4168        | 3.01  | 1184  | 0.4009          | 0.8562   |
| 0.0271        | 4.01  | 1480  | 0.4466          | 0.8675   |
| 0.4371        | 5.01  | 1776  | 0.5005          | 0.8498   |
| 0.0269        | 6.01  | 2072  | 0.5684          | 0.8498   |
| 0.0061        | 7.01  | 2368  | 0.5858          | 0.8643   |
| 0.0262        | 8.01  | 2664  | 0.4571          | 0.8901   |
| 0.0003        | 9.01  | 2960  | 0.6348          | 0.8627   |
| 0.0004        | 10.01 | 3256  | 0.6531          | 0.8708   |
| 0.002         | 11.01 | 3552  | 0.5016          | 0.8821   |
| 0.0048        | 12.01 | 3848  | 0.6594          | 0.8578   |
| 0.5788        | 13.01 | 4144  | 0.5558          | 0.8934   |
| 0.0002        | 14.01 | 4440  | 0.6740          | 0.8611   |
| 0.0001        | 15.01 | 4736  | 0.5346          | 0.8950   |
| 0.007         | 16.01 | 5032  | 0.6110          | 0.8885   |
| 0.0001        | 17.01 | 5328  | 0.6141          | 0.8869   |
| 0.0363        | 18.01 | 5624  | 0.5504          | 0.8934   |
| 0.0002        | 19.01 | 5920  | 0.5372          | 0.8982   |
| 0.4009        | 20.01 | 6216  | 0.5422          | 0.8998   |
| 0.0049        | 21.01 | 6512  | 0.5687          | 0.8918   |
| 0.0048        | 22.01 | 6808  | 0.5355          | 0.8982   |
| 0.0           | 23.01 | 7104  | 0.5720          | 0.8982   |
| 0.0038        | 24.01 | 7400  | 0.5421          | 0.9047   |
| 0.0004        | 25.01 | 7696  | 0.6254          | 0.8950   |
| 0.0002        | 26.01 | 7992  | 0.5809          | 0.8998   |
| 0.0031        | 27.01 | 8288  | 0.5869          | 0.8966   |
| 0.0011        | 28.01 | 8584  | 0.6073          | 0.8950   |
| 0.0001        | 29.01 | 8880  | 0.5942          | 0.9015   |
| 0.5734        | 30.01 | 9176  | 0.5724          | 0.8998   |
| 0.0001        | 31.01 | 9472  | 0.5558          | 0.9047   |
| 0.0001        | 32.01 | 9768  | 0.5694          | 0.8982   |
| 0.0           | 33.01 | 10064 | 0.5874          | 0.9031   |
| 0.0           | 34.01 | 10360 | 0.6037          | 0.8966   |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0