---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MCG-NJUvideomae-base-finetuned-kinetics-face
  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. -->

# MCG-NJUvideomae-base-finetuned-kinetics-face

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7299
- Accuracy: 0.9167

## 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: 1
- eval_batch_size: 1
- 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: 6000

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| 1.5489        | 3.0017   | 100  | 1.5109          | 0.6111   |
| 0.6115        | 6.0033   | 200  | 0.5476          | 1.0      |
| 0.042         | 9.005    | 300  | 0.0697          | 1.0      |
| 0.0243        | 13.0017  | 400  | 0.1967          | 0.9167   |
| 1.1668        | 16.0033  | 500  | 0.9849          | 0.8333   |
| 0.1354        | 19.005   | 600  | 0.7017          | 0.8333   |
| 0.8714        | 23.0017  | 700  | 0.0027          | 1.0      |
| 0.0009        | 26.0033  | 800  | 0.2290          | 0.9444   |
| 0.0003        | 29.005   | 900  | 0.1073          | 0.9722   |
| 1.0037        | 33.0017  | 1000 | 1.3122          | 0.75     |
| 0.4275        | 36.0033  | 1100 | 0.0041          | 1.0      |
| 0.0005        | 39.005   | 1200 | 0.3496          | 0.9444   |
| 0.0005        | 43.0017  | 1300 | 0.1043          | 0.9722   |
| 0.0239        | 46.0033  | 1400 | 1.4939          | 0.8333   |
| 0.0174        | 49.005   | 1500 | 0.0428          | 0.9722   |
| 0.001         | 53.0017  | 1600 | 0.6678          | 0.8889   |
| 0.0865        | 56.0033  | 1700 | 0.3907          | 0.9444   |
| 0.9919        | 59.005   | 1800 | 0.0019          | 1.0      |
| 0.0007        | 63.0017  | 1900 | 0.7224          | 0.8889   |
| 0.0002        | 66.0033  | 2000 | 0.0023          | 1.0      |
| 0.0002        | 69.005   | 2100 | 0.2816          | 0.9444   |
| 0.7678        | 73.0017  | 2200 | 1.0904          | 0.8056   |
| 0.0013        | 76.0033  | 2300 | 1.0187          | 0.8611   |
| 0.0004        | 79.005   | 2400 | 0.2458          | 0.9722   |
| 0.0094        | 83.0017  | 2500 | 0.9661          | 0.8333   |
| 0.0003        | 86.0033  | 2600 | 0.2274          | 0.9722   |
| 0.0011        | 89.005   | 2700 | 0.2844          | 0.9444   |
| 0.0001        | 93.0017  | 2800 | 0.8405          | 0.8611   |
| 0.0001        | 96.0033  | 2900 | 0.5875          | 0.9167   |
| 0.0001        | 99.005   | 3000 | 1.8345          | 0.7778   |
| 0.0001        | 103.0017 | 3100 | 0.5098          | 0.9167   |
| 0.0003        | 106.0033 | 3200 | 0.0062          | 1.0      |
| 0.3248        | 109.005  | 3300 | 0.4113          | 0.9444   |
| 0.0001        | 113.0017 | 3400 | 0.1064          | 0.9722   |
| 0.0001        | 116.0033 | 3500 | 0.0006          | 1.0      |
| 0.0003        | 119.005  | 3600 | 0.2552          | 0.9722   |
| 0.001         | 123.0017 | 3700 | 0.0202          | 1.0      |
| 0.0002        | 126.0033 | 3800 | 0.3475          | 0.9444   |
| 0.0001        | 129.005  | 3900 | 0.5493          | 0.9444   |
| 0.0001        | 133.0017 | 4000 | 0.5506          | 0.9444   |
| 0.0001        | 136.0033 | 4100 | 0.5711          | 0.9167   |
| 0.0001        | 139.005  | 4200 | 0.5181          | 0.9444   |
| 0.0021        | 143.0017 | 4300 | 0.7568          | 0.9167   |
| 0.9007        | 146.0033 | 4400 | 0.0072          | 1.0      |
| 0.0001        | 149.005  | 4500 | 0.2858          | 0.9444   |
| 0.0001        | 153.0017 | 4600 | 1.0247          | 0.8889   |
| 0.6131        | 156.0033 | 4700 | 0.0814          | 0.9722   |
| 0.0004        | 159.005  | 4800 | 1.8986          | 0.8056   |
| 0.0001        | 163.0017 | 4900 | 1.5607          | 0.8056   |
| 0.0001        | 166.0033 | 5000 | 1.5370          | 0.8056   |
| 0.0001        | 169.005  | 5100 | 1.4807          | 0.8056   |
| 0.0001        | 173.0017 | 5200 | 1.2996          | 0.8333   |
| 0.0           | 176.0033 | 5300 | 1.2259          | 0.8056   |
| 0.0001        | 179.005  | 5400 | 1.1819          | 0.8056   |
| 0.0           | 183.0017 | 5500 | 1.1047          | 0.8056   |
| 0.0001        | 186.0033 | 5600 | 1.0461          | 0.8333   |
| 0.0           | 189.005  | 5700 | 1.2544          | 0.8056   |
| 0.8628        | 193.0017 | 5800 | 1.1260          | 0.8056   |
| 0.0           | 196.0033 | 5900 | 0.7299          | 0.9167   |
| 0.0           | 199.005  | 6000 | 0.7299          | 0.9167   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1