Instructions to use Mena55/videomae-base-finetuned-kinetics_m_v11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mena55/videomae-base-finetuned-kinetics_m_v11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="Mena55/videomae-base-finetuned-kinetics_m_v11")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("Mena55/videomae-base-finetuned-kinetics_m_v11") model = AutoModelForVideoClassification.from_pretrained("Mena55/videomae-base-finetuned-kinetics_m_v11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
videomae-base-finetuned-kinetics_m_v11
This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0009
- eval_accuracy: 1.0
- eval_runtime: 26.6647
- eval_samples_per_second: 0.75
- eval_steps_per_second: 0.188
- epoch: 2.2
- step: 261
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: 430
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
MCG-NJU/videomae-base-finetuned-kinetics