Instructions to use mitegvg/videomae-tiny-finetuned-xd-violence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitegvg/videomae-tiny-finetuned-xd-violence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="mitegvg/videomae-tiny-finetuned-xd-violence")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("mitegvg/videomae-tiny-finetuned-xd-violence") model = AutoModelForVideoClassification.from_pretrained("mitegvg/videomae-tiny-finetuned-xd-violence") - Notebooks
- Google Colab
- Kaggle
videomae-tiny-finetuned-xd-violence
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2757
- Accuracy: 0.6470
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: 1580
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6576 | 0.25 | 395 | 1.5471 | 0.6037 |
| 1.4533 | 1.25 | 790 | 1.2815 | 0.6548 |
| 1.5216 | 2.25 | 1185 | 1.3293 | 0.6363 |
| 1.3845 | 3.25 | 1580 | 1.2757 | 0.6470 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.1.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
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