---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ctsinov1
  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. -->

# ctsinov1

This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7987
- Accuracy: 0.8586

## 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: 2
- eval_batch_size: 2
- 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: 0.1
- training_steps: 17500

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5787        | 0.02  | 350   | 0.5787          | 0.7104   |
| 0.5175        | 1.02  | 700   | 0.7402          | 0.8081   |
| 0.4062        | 2.02  | 1050  | 0.8532          | 0.8283   |
| 0.7962        | 3.02  | 1400  | 0.7184          | 0.8114   |
| 0.8225        | 4.02  | 1750  | 1.6868          | 0.5657   |
| 0.724         | 5.02  | 2100  | 1.0066          | 0.7508   |
| 0.1468        | 6.02  | 2450  | 0.7703          | 0.8316   |
| 0.8406        | 7.02  | 2800  | 0.5863          | 0.8485   |
| 0.4485        | 8.02  | 3150  | 0.6602          | 0.8384   |
| 0.0134        | 9.02  | 3500  | 0.6907          | 0.8316   |
| 0.11          | 10.02 | 3850  | 0.7098          | 0.8316   |
| 0.6557        | 11.02 | 4200  | 0.6507          | 0.8384   |
| 0.2642        | 12.02 | 4550  | 0.6555          | 0.8519   |
| 0.2413        | 13.02 | 4900  | 0.6481          | 0.8519   |
| 0.6278        | 14.02 | 5250  | 0.6555          | 0.8552   |
| 0.0107        | 15.02 | 5600  | 0.6550          | 0.8519   |
| 0.3013        | 16.02 | 5950  | 0.7405          | 0.8485   |
| 0.5055        | 17.02 | 6300  | 0.6563          | 0.8451   |
| 0.0059        | 18.02 | 6650  | 0.6917          | 0.8485   |
| 0.4332        | 19.02 | 7000  | 0.6888          | 0.8384   |
| 0.2602        | 20.02 | 7350  | 0.7993          | 0.8418   |
| 0.2142        | 21.02 | 7700  | 0.7131          | 0.8451   |
| 0.5742        | 22.02 | 8050  | 0.9735          | 0.7980   |
| 0.2504        | 23.02 | 8400  | 0.8314          | 0.8384   |
| 0.8514        | 24.02 | 8750  | 0.7481          | 0.8418   |
| 0.8148        | 25.02 | 9100  | 0.7210          | 0.8384   |
| 0.2594        | 26.02 | 9450  | 0.9980          | 0.8249   |
| 0.6742        | 27.02 | 9800  | 0.7987          | 0.8586   |
| 0.0063        | 28.02 | 10150 | 0.9369          | 0.8316   |
| 0.5186        | 29.02 | 10500 | 1.0871          | 0.8148   |
| 0.3076        | 30.02 | 10850 | 0.8931          | 0.8350   |
| 0.1113        | 31.02 | 11200 | 1.0014          | 0.8384   |
| 0.2201        | 32.02 | 11550 | 0.8628          | 0.8485   |
| 0.0324        | 33.02 | 11900 | 0.9972          | 0.8350   |
| 0.4411        | 34.02 | 12250 | 1.0592          | 0.8350   |
| 0.0011        | 35.02 | 12600 | 1.0746          | 0.8283   |
| 0.3917        | 36.02 | 12950 | 0.9696          | 0.8384   |
| 0.7268        | 37.02 | 13300 | 1.1062          | 0.8182   |
| 0.3747        | 38.02 | 13650 | 1.0368          | 0.8350   |
| 0.5584        | 39.02 | 14000 | 1.0149          | 0.8418   |
| 0.4637        | 40.02 | 14350 | 1.0104          | 0.8316   |
| 0.0014        | 41.02 | 14700 | 1.0437          | 0.8418   |
| 0.6253        | 42.02 | 15050 | 1.1687          | 0.8148   |
| 0.0009        | 43.02 | 15400 | 1.0243          | 0.8418   |
| 0.0003        | 44.02 | 15750 | 1.0864          | 0.8316   |
| 0.291         | 45.02 | 16100 | 1.0647          | 0.8384   |
| 0.4962        | 46.02 | 16450 | 1.1166          | 0.8316   |
| 0.0919        | 47.02 | 16800 | 1.1209          | 0.8283   |
| 0.0007        | 48.02 | 17150 | 1.1260          | 0.8316   |
| 0.0008        | 49.02 | 17500 | 1.1139          | 0.8350   |


### Framework versions

- Transformers 4.56.2
- Pytorch 2.6.0+cu118
- Datasets 2.2.1
- Tokenizers 0.22.1