videomae-large_ActionRecognition

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videomae-large_ActionRecognition

This model is a fine-tuned version of MCG-NJU/videomae-large on an unknown dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.0503
• eval_confusion_matrix: {'confusion_matrix': array([[14, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 12, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 17, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 23, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 1, 32, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 10, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 12, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 7, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 22]])}
• eval_runtime: 27.1769
• eval_samples_per_second: 5.703
• eval_steps_per_second: 2.87
• step: 0

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: 900

Framework versions

• Transformers 4.39.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2