Instructions to use shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced") model = AutoModelForVideoClassification.from_pretrained("shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced") - Notebooks
- Google Colab
- Kaggle
videomae-base-finetuned-ucf-crimevbinary-balanced
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: 0.3486
- Accuracy: 0.8611
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: 3e-05
- 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_steps: 500
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.719 | 1.0 | 29 | 0.6348 | 0.6944 |
| 0.5756 | 2.0 | 58 | 0.5475 | 0.75 |
| 0.4718 | 3.0 | 87 | 0.4779 | 0.7778 |
| 0.4986 | 4.0 | 116 | 0.4849 | 0.8056 |
| 0.3705 | 5.0 | 145 | 0.4065 | 0.8056 |
| 0.3042 | 6.0 | 174 | 0.5558 | 0.8333 |
| 0.2333 | 7.0 | 203 | 0.4220 | 0.8889 |
| 0.4724 | 8.0 | 232 | 0.2888 | 0.8333 |
| 0.0119 | 9.0 | 261 | 0.5093 | 0.8889 |
| 0.2417 | 10.0 | 290 | 0.7827 | 0.7778 |
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced
Base model
MCG-NJU/videomae-base