Instructions to use mayarmostafa/videomae-base-finetuned-bleeding-exp_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayarmostafa/videomae-base-finetuned-bleeding-exp_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="mayarmostafa/videomae-base-finetuned-bleeding-exp_6")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("mayarmostafa/videomae-base-finetuned-bleeding-exp_6") model = AutoModelForVideoClassification.from_pretrained("mayarmostafa/videomae-base-finetuned-bleeding-exp_6") - Notebooks
- Google Colab
- Kaggle
videomae-base-finetuned-bleeding-exp_6
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset.
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: 4
- eval_batch_size: 4
- 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: 6000
Framework versions
- Transformers 4.40.2
- Pytorch 1.12.0
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for mayarmostafa/videomae-base-finetuned-bleeding-exp_6
Base model
MCG-NJU/videomae-base