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# videomae-base-short-finetuned-ssv2-finetuned-rwf2000-epochs8-batch8-fp16

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: 1.4339
- Accuracy: 0.4643

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4239        | 0.06  | 200  | 0.3879          | 0.82     |
| 0.4179        | 1.06  | 400  | 1.1635          | 0.6162   |
| 0.4329        | 2.06  | 600  | 0.8215          | 0.63     |
| 0.3051        | 3.06  | 800  | 0.5541          | 0.7412   |
| 0.172         | 4.06  | 1000 | 0.4696          | 0.8363   |
| 0.1955        | 5.06  | 1200 | 0.5384          | 0.78     |
| 0.2301        | 6.06  | 1400 | 1.3358          | 0.635    |
| 0.2995        | 7.06  | 1600 | 1.0372          | 0.7087   |
| 0.3789        | 8.06  | 1800 | 0.8670          | 0.7412   |
| 0.2525        | 9.06  | 2000 | 0.5886          | 0.8225   |
| 0.1846        | 10.06 | 2200 | 0.7851          | 0.735    |
| 0.1547        | 11.06 | 2400 | 0.8905          | 0.7638   |
| 0.2501        | 12.06 | 2600 | 0.9807          | 0.76     |
| 0.1046        | 13.06 | 2800 | 1.0419          | 0.7438   |
| 0.0786        | 14.06 | 3000 | 1.0128          | 0.7538   |
| 0.0178        | 15.06 | 3200 | 1.0156          | 0.75     |


### 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-fp16", "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-fp16
========================================================================


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: 1.4339
* Accuracy: 0.4643


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
* mixed\_precision\_training: Native AMP


### 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
* mixed\_precision\_training: Native AMP ### 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
* mixed\_precision\_training: Native AMP ### 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
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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