videomae-large_Sports_action_recognition

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videomae-large_Sports_action_recognition

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: 0.0027
• eval_confusion_matrix: {'confusion_matrix': array([[24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 37, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 43, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 72, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 28, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36]])}
• eval_runtime: 127.0184
• eval_samples_per_second: 3.007
• eval_steps_per_second: 1.504
• 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: 17150

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2