Instructions to use saba003/videomae-base-finetuned-ucf101-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saba003/videomae-base-finetuned-ucf101-subset with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, CustomVideoMAEForVideoClassification processor = AutoImageProcessor.from_pretrained("saba003/videomae-base-finetuned-ucf101-subset") model = CustomVideoMAEForVideoClassification.from_pretrained("saba003/videomae-base-finetuned-ucf101-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2213
- Accuracy: 0.2839
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: 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: 148
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.4441 | 0.2568 | 38 | 2.3019 | 0.2857 |
| 1.9821 | 1.2568 | 76 | 2.1692 | 0.1143 |
| 1.9084 | 2.2568 | 114 | 2.2854 | 0.2 |
| 1.783 | 3.2297 | 148 | 2.1290 | 0.2143 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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