Instructions to use Ham1mad1/videomae-base-Vsl-Lab-PC-V6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ham1mad1/videomae-base-Vsl-Lab-PC-V6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="Ham1mad1/videomae-base-Vsl-Lab-PC-V6")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("Ham1mad1/videomae-base-Vsl-Lab-PC-V6") model = AutoModelForVideoClassification.from_pretrained("Ham1mad1/videomae-base-Vsl-Lab-PC-V6") - Notebooks
- Google Colab
- Kaggle
videomae-base-Vsl-Lab-PC-V6
This model was trained from scratch 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: 1
- eval_batch_size: 1
- 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: 40350
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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