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# videomae-base-finetuned-sign-subset

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: 3.3672
- Accuracy: 0.1905

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 270
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.04  | 11   | 2.4220          | 0.0870   |
| 2.3491        | 1.04  | 22   | 2.6315          | 0.0      |
| 2.3491        | 2.04  | 33   | 2.6680          | 0.0435   |
| 2.2285        | 3.04  | 44   | 2.8487          | 0.1304   |
| 2.2285        | 4.04  | 55   | 3.0361          | 0.0870   |
| 1.996         | 5.04  | 66   | 3.0258          | 0.1304   |
| 1.996         | 6.04  | 77   | 3.2125          | 0.1304   |
| 1.6956        | 7.04  | 88   | 3.2063          | 0.1304   |
| 1.6956        | 8.04  | 99   | 3.1919          | 0.1304   |
| 1.5088        | 9.04  | 110  | 3.1940          | 0.1304   |
| 1.3777        | 10.04 | 121  | 3.3180          | 0.1739   |
| 1.3777        | 11.04 | 132  | 3.3112          | 0.1304   |
| 1.1509        | 12.04 | 143  | 3.3400          | 0.1304   |
| 1.1509        | 13.04 | 154  | 3.2550          | 0.1739   |
| 0.9036        | 14.04 | 165  | 3.3682          | 0.1304   |
| 0.9036        | 15.04 | 176  | 3.3775          | 0.1304   |
| 0.8303        | 16.04 | 187  | 3.4701          | 0.1304   |
| 0.8303        | 17.04 | 198  | 3.4340          | 0.1739   |
| 0.6683        | 18.04 | 209  | 3.4843          | 0.1304   |
| 0.5126        | 19.04 | 220  | 3.3552          | 0.2174   |
| 0.5126        | 20.04 | 231  | 3.3702          | 0.2609   |
| 0.3728        | 21.04 | 242  | 3.3871          | 0.2609   |
| 0.3728        | 22.04 | 253  | 3.3565          | 0.2609   |
| 0.3291        | 23.04 | 264  | 3.3861          | 0.3043   |
| 0.3291        | 24.02 | 270  | 3.3876          | 0.3043   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "videomae-base-finetuned-sign-subset", "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-sign-subset
===================================


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: 3.3672
* Accuracy: 0.1905


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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 270
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 270
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 270
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 270
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2