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

# timeSformer

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: 7.8889
- Accuracy: 0.0052

## 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: 0.005
- train_batch_size: 288
- eval_batch_size: 288
- 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: 740

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 3.8418        | 0.0135  | 10   | 5.5898          | 0.0122   |
| 3.1132        | 1.0135  | 20   | 5.8064          | 0.0052   |
| 3.1265        | 2.0135  | 30   | 5.9893          | 0.0087   |
| 3.1378        | 3.0135  | 40   | 5.8669          | 0.0017   |
| 3.1408        | 4.0135  | 50   | 6.0782          | 0.0      |
| 3.0995        | 5.0135  | 60   | 5.9832          | 0.0      |
| 3.0778        | 6.0135  | 70   | 5.6949          | 0.0486   |
| 3.0765        | 7.0135  | 80   | 5.8415          | 0.0017   |
| 3.0689        | 8.0135  | 90   | 5.9740          | 0.0017   |
| 3.0093        | 9.0135  | 100  | 5.8951          | 0.0052   |
| 3.021         | 10.0135 | 110  | 6.1976          | 0.0052   |
| 2.9475        | 11.0135 | 120  | 6.4536          | 0.0191   |
| 2.9468        | 12.0135 | 130  | 6.5053          | 0.0      |
| 2.9593        | 13.0135 | 140  | 6.1705          | 0.0139   |
| 2.9632        | 14.0135 | 150  | 6.5190          | 0.0017   |
| 2.9551        | 15.0135 | 160  | 6.6961          | 0.0017   |
| 3.0157        | 16.0135 | 170  | 6.0960          | 0.0208   |
| 3.0034        | 17.0135 | 180  | 6.5592          | 0.0052   |
| 2.9783        | 18.0135 | 190  | 6.4399          | 0.0365   |
| 2.9608        | 19.0135 | 200  | 6.4748          | 0.0035   |
| 2.9909        | 20.0135 | 210  | 6.3702          | 0.0035   |
| 2.9598        | 21.0135 | 220  | 6.5077          | 0.0      |
| 2.9552        | 22.0135 | 230  | 7.4796          | 0.0069   |
| 3.0327        | 23.0135 | 240  | 6.5649          | 0.0      |
| 3.0028        | 24.0135 | 250  | 6.6634          | 0.0087   |
| 2.9237        | 25.0135 | 260  | 6.8856          | 0.0      |
| 2.9411        | 26.0135 | 270  | 6.9620          | 0.0017   |
| 2.9281        | 27.0135 | 280  | 6.9007          | 0.0069   |
| 2.9255        | 28.0135 | 290  | 7.0261          | 0.0017   |
| 2.9621        | 29.0135 | 300  | 6.5056          | 0.0      |
| 2.9419        | 30.0135 | 310  | 6.9045          | 0.0035   |
| 2.978         | 31.0135 | 320  | 6.8059          | 0.0122   |
| 3.0299        | 32.0135 | 330  | 6.6262          | 0.0017   |
| 3.0313        | 33.0135 | 340  | 6.6773          | 0.0      |
| 2.995         | 34.0135 | 350  | 6.7126          | 0.0      |
| 2.9588        | 35.0135 | 360  | 6.4966          | 0.0035   |
| 2.9784        | 36.0135 | 370  | 6.2595          | 0.0      |
| 3.0047        | 37.0135 | 380  | 6.5504          | 0.0      |
| 2.98          | 38.0135 | 390  | 6.3898          | 0.0017   |
| 2.9568        | 39.0135 | 400  | 6.8740          | 0.0017   |
| 2.9418        | 40.0135 | 410  | 6.5854          | 0.0      |
| 2.955         | 41.0135 | 420  | 6.5085          | 0.0052   |
| 2.9689        | 42.0135 | 430  | 6.4343          | 0.0      |
| 2.9494        | 43.0135 | 440  | 6.5746          | 0.0      |
| 2.9363        | 44.0135 | 450  | 6.7996          | 0.0035   |
| 2.9002        | 45.0135 | 460  | 7.1057          | 0.0035   |
| 2.8776        | 46.0135 | 470  | 7.4000          | 0.0035   |
| 2.8853        | 47.0135 | 480  | 6.9539          | 0.0017   |
| 2.8618        | 48.0135 | 490  | 7.0917          | 0.0035   |
| 2.8728        | 49.0135 | 500  | 7.4421          | 0.0017   |
| 2.8888        | 50.0135 | 510  | 7.1914          | 0.0052   |
| 2.8824        | 51.0135 | 520  | 7.3226          | 0.0069   |
| 2.8932        | 52.0135 | 530  | 7.3581          | 0.0      |
| 2.8562        | 53.0135 | 540  | 7.3102          | 0.0017   |
| 2.8651        | 54.0135 | 550  | 7.2675          | 0.0035   |
| 2.8898        | 55.0135 | 560  | 7.5446          | 0.0017   |
| 2.8931        | 56.0135 | 570  | 7.2915          | 0.0      |
| 2.8864        | 57.0135 | 580  | 7.2397          | 0.0017   |
| 2.8683        | 58.0135 | 590  | 6.9988          | 0.0017   |
| 2.8213        | 59.0135 | 600  | 7.4490          | 0.0052   |
| 2.8319        | 60.0135 | 610  | 7.5569          | 0.0017   |
| 2.8237        | 61.0135 | 620  | 7.5322          | 0.0017   |
| 2.8057        | 62.0135 | 630  | 7.6815          | 0.0122   |
| 2.8047        | 63.0135 | 640  | 7.3165          | 0.0      |
| 2.8277        | 64.0135 | 650  | 7.4256          | 0.0069   |
| 2.8202        | 65.0135 | 660  | 7.6688          | 0.0035   |
| 2.772         | 66.0135 | 670  | 7.5628          | 0.0052   |
| 2.7749        | 67.0135 | 680  | 7.7103          | 0.0052   |
| 2.7442        | 68.0135 | 690  | 7.8151          | 0.0052   |
| 2.7715        | 69.0135 | 700  | 7.8429          | 0.0052   |
| 2.7593        | 70.0135 | 710  | 7.8079          | 0.0052   |
| 2.7334        | 71.0135 | 720  | 7.8237          | 0.0052   |
| 2.7392        | 72.0135 | 730  | 7.9085          | 0.0052   |
| 2.753         | 73.0135 | 740  | 7.8889          | 0.0052   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1