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# videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8

This model is a fine-tuned version of [MCG-NJU/videomae-base-short-finetuned-ssv2](https://huggingface.co/MCG-NJU/videomae-base-short-finetuned-ssv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7821
- Accuracy: 0.6713

## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4247        | 0.06  | 200  | 0.4205          | 0.8063   |
| 0.4125        | 1.06  | 400  | 0.6749          | 0.72     |
| 0.3265        | 2.06  | 600  | 1.3838          | 0.5763   |
| 0.2204        | 3.06  | 800  | 0.6725          | 0.7275   |
| 0.2965        | 4.06  | 1000 | 0.4583          | 0.8263   |
| 0.1883        | 5.06  | 1200 | 0.3786          | 0.8488   |
| 0.1321        | 6.06  | 1400 | 1.6632          | 0.5962   |
| 0.369         | 7.06  | 1600 | 0.6018          | 0.8063   |
| 0.3764        | 8.06  | 1800 | 0.8546          | 0.74     |
| 0.2401        | 9.06  | 2000 | 0.5422          | 0.825    |
| 0.1943        | 10.06 | 2200 | 0.5868          | 0.8113   |
| 0.1352        | 11.06 | 2400 | 0.7111          | 0.8063   |
| 0.2276        | 12.06 | 2600 | 0.8847          | 0.7812   |
| 0.149         | 13.06 | 2800 | 0.8581          | 0.7837   |
| 0.0848        | 14.06 | 3000 | 0.8707          | 0.7788   |
| 0.046         | 15.06 | 3200 | 0.7914          | 0.7963   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8", "results": []}]}
transformers pytorch tensorboard videomae video-classification generated_from_trainer license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #videomae #video-classification #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8
===================================================================


This model is a fine-tuned version of MCG-NJU/videomae-base-short-finetuned-ssv2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7821
* Accuracy: 0.6713


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
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 3200


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* 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
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 3200 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 3200 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 3200### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2