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# videomae-base-finetuned-ucf_crime

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: 1.4776
- Accuracy: 0.3720

## 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: 8
- eval_batch_size: 5
- 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: 640

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9349        | 0.06  | 40   | 1.9845          | 0.2332   |
| 1.8834        | 1.06  | 80   | 1.9134          | 0.3009   |
| 1.7506        | 2.06  | 120  | 1.8460          | 0.3080   |
| 1.6494        | 3.06  | 160  | 1.7691          | 0.2624   |
| 1.6189        | 4.06  | 200  | 1.7939          | 0.2537   |
| 1.6895        | 5.06  | 240  | 1.7809          | 0.2706   |
| 1.517         | 6.06  | 280  | 1.6773          | 0.3244   |
| 1.308         | 7.06  | 320  | 1.8364          | 0.3152   |
| 1.2267        | 8.06  | 360  | 2.0392          | 0.2440   |
| 1.4347        | 9.06  | 400  | 1.9110          | 0.2450   |
| 1.1567        | 10.06 | 440  | 1.7606          | 0.2840   |
| 1.1937        | 11.06 | 480  | 1.9803          | 0.2737   |
| 1.0729        | 12.06 | 520  | 1.8355          | 0.3352   |
| 1.0721        | 13.06 | 560  | 1.7808          | 0.3311   |
| 0.6594        | 14.06 | 600  | 1.8175          | 0.3060   |
| 0.7636        | 15.06 | 640  | 1.8409          | 0.3409   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "videomae-base-finetuned-ucf_crime", "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-finetuned-ucf\_crime
==================================


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: 1.4776
* Accuracy: 0.3720


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: 8
* eval\_batch\_size: 5
* 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: 640


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 5
* 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: 640 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 8
* eval\_batch\_size: 5
* 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: 640 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3