Instructions to use ali5341/videomae-base-finetuned-ucf101-subset-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ali5341/videomae-base-finetuned-ucf101-subset-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="ali5341/videomae-base-finetuned-ucf101-subset-finetuned")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("ali5341/videomae-base-finetuned-ucf101-subset-finetuned") model = AutoModelForVideoClassification.from_pretrained("ali5341/videomae-base-finetuned-ucf101-subset-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
videomae-base-finetuned-ucf101-subset-finetuned
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9147
- Accuracy: 0.7701
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: 2
- eval_batch_size: 2
- 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: 29760
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.537 | 0.2 | 5952 | 1.8361 | 0.4807 |
| 0.8794 | 1.2 | 11904 | 1.6076 | 0.5795 |
| 0.3028 | 2.2 | 17856 | 1.5198 | 0.6318 |
| 0.2322 | 3.2 | 23808 | 0.9564 | 0.7515 |
| 0.4151 | 4.2 | 29760 | 0.9741 | 0.7595 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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