Instructions to use AbdullahTheScientist/videomae-base-finetuned-ucf101-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbdullahTheScientist/videomae-base-finetuned-ucf101-subset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="AbdullahTheScientist/videomae-base-finetuned-ucf101-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("AbdullahTheScientist/videomae-base-finetuned-ucf101-subset") model = AutoModelForVideoClassification.from_pretrained("AbdullahTheScientist/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 MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6844
- Accuracy: 0.5
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: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
- training_steps: 72
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7166 | 0.2639 | 19 | 0.8425 | 0.4706 |
| 0.7454 | 1.2639 | 38 | 0.6769 | 0.4824 |
| 0.6962 | 2.2639 | 57 | 0.6988 | 0.5059 |
| 0.6676 | 3.2083 | 72 | 0.6941 | 0.5059 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
MCG-NJU/videomae-base