|
|
---
|
|
|
library_name: transformers
|
|
|
license: cc-by-nc-4.0
|
|
|
base_model: MCG-NJU/videomae-base-finetuned-ssv2
|
|
|
tags:
|
|
|
- generated_from_trainer
|
|
|
metrics:
|
|
|
- accuracy
|
|
|
- f1
|
|
|
- precision
|
|
|
- recall
|
|
|
model-index:
|
|
|
- name: videomae-base-ssv2-binary-finetuned-xd-violence
|
|
|
results: []
|
|
|
---
|
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
should probably proofread and complete it, then remove this comment. -->
|
|
|
|
|
|
# videomae-base-ssv2-binary-finetuned-xd-violence
|
|
|
|
|
|
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-ssv2](https://huggingface.co/MCG-NJU/videomae-base-finetuned-ssv2) on an unknown dataset.
|
|
|
It achieves the following results on the evaluation set:
|
|
|
- Loss: 0.6173
|
|
|
- Accuracy: 0.6712
|
|
|
- F1: 0.4986
|
|
|
- Precision: 0.5807
|
|
|
- Recall: 0.4368
|
|
|
- Specificity: 0.8114
|
|
|
- True Positives: 5887
|
|
|
- True Negatives: 18284
|
|
|
- False Positives: 4250
|
|
|
- False Negatives: 7590
|
|
|
|
|
|
## 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: 0.0005
|
|
|
- train_batch_size: 8
|
|
|
- eval_batch_size: 8
|
|
|
- seed: 42
|
|
|
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
|
|
- lr_scheduler_type: linear
|
|
|
- lr_scheduler_warmup_ratio: 0.1
|
|
|
- training_steps: 1684
|
|
|
|
|
|
### Training results
|
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Specificity | True Positives | True Negatives | False Positives | False Negatives |
|
|
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------:|:--------------:|:--------------:|:---------------:|:---------------:|
|
|
|
| 0.6785 | 0.2506 | 422 | 0.6556 | 0.6638 | 0.4328 | 0.5870 | 0.3427 | 0.8558 | 4619 | 19284 | 3250 | 8858 |
|
|
|
| 0.6774 | 1.2506 | 844 | 0.6571 | 0.6407 | 0.5602 | 0.5169 | 0.6115 | 0.6582 | 8241 | 14832 | 7702 | 5236 |
|
|
|
| 0.6654 | 2.2506 | 1266 | 0.6169 | 0.6635 | 0.5385 | 0.5532 | 0.5246 | 0.7466 | 7070 | 16823 | 5711 | 6407 |
|
|
|
| 0.5013 | 3.2482 | 1684 | 0.6173 | 0.6712 | 0.4986 | 0.5807 | 0.4368 | 0.8114 | 5887 | 18284 | 4250 | 7590 |
|
|
|
|
|
|
|
|
|
### Framework versions
|
|
|
|
|
|
- Transformers 4.51.3
|
|
|
- Pytorch 2.1.0+cu118
|
|
|
- Datasets 3.6.0
|
|
|
- Tokenizers 0.21.1
|
|
|
|
