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# videomae-base-finetuned-engine-subset-20230313

This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8913
- Accuracy: 0.6745

## 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: 6
- eval_batch_size: 6
- 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: 1110

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6212        | 0.03  | 38   | 2.3629          | 0.3774   |
| 2.455         | 1.03  | 76   | 2.3674          | 0.2170   |
| 2.4311        | 2.03  | 114  | 2.2191          | 0.3231   |
| 2.2768        | 3.03  | 152  | 2.1227          | 0.3608   |
| 1.7528        | 4.03  | 190  | 1.7296          | 0.4363   |
| 1.5381        | 5.03  | 228  | 1.5016          | 0.4340   |
| 1.407         | 6.03  | 266  | 1.2878          | 0.5448   |
| 1.1053        | 7.03  | 304  | 1.5210          | 0.4009   |
| 1.0893        | 8.03  | 342  | 1.3902          | 0.4623   |
| 0.8136        | 9.03  | 380  | 1.6456          | 0.4033   |
| 0.9565        | 10.03 | 418  | 1.1826          | 0.5613   |
| 1.0147        | 11.03 | 456  | 1.2099          | 0.5118   |
| 0.9125        | 12.03 | 494  | 1.1850          | 0.5495   |
| 0.7091        | 13.03 | 532  | 1.2324          | 0.5354   |
| 0.7361        | 14.03 | 570  | 1.0225          | 0.6226   |
| 0.6979        | 15.03 | 608  | 1.0738          | 0.5590   |
| 0.5265        | 16.03 | 646  | 1.1062          | 0.5873   |
| 0.5651        | 17.03 | 684  | 1.1402          | 0.5802   |
| 0.7182        | 18.03 | 722  | 1.0974          | 0.5802   |
| 0.6582        | 19.03 | 760  | 1.0529          | 0.6179   |
| 0.5709        | 20.03 | 798  | 0.9655          | 0.6344   |
| 0.4808        | 21.03 | 836  | 1.0441          | 0.6226   |
| 0.5816        | 22.03 | 874  | 0.9445          | 0.6439   |
| 0.5057        | 23.03 | 912  | 1.0248          | 0.6321   |
| 0.6253        | 24.03 | 950  | 0.9518          | 0.6604   |
| 0.6841        | 25.03 | 988  | 0.8913          | 0.6745   |
| 0.5933        | 26.03 | 1026 | 0.9013          | 0.6439   |
| 0.389         | 27.03 | 1064 | 0.9090          | 0.6627   |
| 0.3705        | 28.03 | 1102 | 0.8936          | 0.6722   |
| 0.6043        | 29.01 | 1110 | 0.8942          | 0.6722   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "videomae-base-finetuned-engine-subset-20230313", "results": []}]}
transformers pytorch videomae video-classification generated_from_trainer license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #videomae #video-classification #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

videomae-base-finetuned-engine-subset-20230313
==============================================


This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8913
* Accuracy: 0.6745


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: 6
* eval\_batch\_size: 6
* 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: 1110


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* 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: 1110 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #videomae #video-classification #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* 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: 1110 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #videomae #video-classification #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* 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: 1110### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2