File size: 1,932 Bytes
289733a c58a4a2 289733a dc6cd10 d4bbe61 ef563f6 289733a aaa8f84 289733a aaa8f84 289733a aaa8f84 289733a c58a4a2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-fight-nofight-subset2
results: []
datasets:
- Pinwheel/ActsOfAgression
---
<!-- 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-finetuned-fight-nofight-subset2
**NOTE: This is experimentational if youre expecting this to work accurately (it wont) or be useful should probably look eslewhere😛**
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on the [Acts of Agression (cttv footage fights)](https://huggingface.co/datasets/Pinwheel/ActsOfAgression) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5190
- Accuracy: 0.7435
## Model description
Classifies video input into "Fight" or "No Fight" Class
## Intended uses & limitations
Can be used to detect fights/crime in cctv footage
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 252
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5145 | 0.25 | 64 | 0.7845 | 0.5075 |
| 0.607 | 1.25 | 128 | 0.6886 | 0.6343 |
| 0.3986 | 2.25 | 192 | 0.5106 | 0.7463 |
| 0.3632 | 3.24 | 252 | 0.7408 | 0.6716 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1 |