Instructions to use rickysk/videomae-base-ipm_first_videos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rickysk/videomae-base-ipm_first_videos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="rickysk/videomae-base-ipm_first_videos")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("rickysk/videomae-base-ipm_first_videos") model = AutoModelForVideoClassification.from_pretrained("rickysk/videomae-base-ipm_first_videos") - Notebooks
- Google Colab
- Kaggle
videomae-base-ipm_first_videos
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:
- eval_loss: 0.6246
- eval_accuracy: 0.7895
- eval_runtime: 50.9021
- eval_samples_per_second: 0.747
- eval_steps_per_second: 0.196
- epoch: 48.01
- step: 146
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: 330
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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