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Videomae Base Finetuned Kinetics 0328 Final 253259

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: Loss: 0.5132 Accuracy: 0.8633

KindOtherBase modelMCG-NJU/videomae-base-finetuned-kineticsVersionRevision aad5648

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base-finetuned-kinetics
Version
vRevision aad5648
Updated
Sep 18, 2026
Downloads
6
Source
Hugging Face

Source description

videomae-base-finetuned-kinetics-0328_final_253259

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:

  • Loss: 0.5132
  • Accuracy: 0.8633

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • training_steps: 72700

Training results

Training LossEpochStepValidation LossAccuracy
0.15930.01007280.71660.7523
0.00761.010014560.33800.9012
0.00342.010021840.34500.9037
0.00293.010029120.67050.8041
0.00184.010036400.85370.8140
0.10485.010043680.41600.8872
0.01866.010050960.79270.8107
0.00177.010058240.70840.8123
0.00038.010065520.75710.8560
0.0039.010072800.43140.8988
0.000410.010080080.92040.8165
0.000211.010087361.27020.7901
0.000212.010094640.58250.8831
0.000413.0100101920.90330.8288
0.000214.0100109200.92590.8527
0.005115.0100116481.01720.8337
0.000216.0100123761.07180.8214
0.017.0100131041.02140.8444

Framework versions

  • Transformers 4.48.1
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0

Sources

1 source
Hugging FacePrimaryVersion aad5648c06c9b594563ce222314aac7b4a80e52d
huggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0328_final_253259/tree/aad5648c06c9b594563ce222314aac7b4a80e52d

Verified Sep 18

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 4df38edeab50…6c10 · Hugging Face

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Source excerpts

2 excerpts

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-kinetics-0328_final_253259 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-kinetics-0328_final_253259 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5132 - Accuracy: 0.8633 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - training_steps: 72700 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:-----:|:---------------:|:--------:| | 0.1593 | 0.0100 | 728 | 0.7166 | 0.7523 | | 0.0076 | 1.0100 | 1456 | 0.3380 | 0.9012 | | 0.0034 | 2.0100 | 2184 | 0.3450 | 0.9037 | | 0.0029 | 3.0100 | 2912 | 0.6705 | 0.8041 | | 0.0018 | 4.0100 | 3640 | 0.8537 | 0.8140 | | 0.1048 | 5.0100 | 4368 | 0.4160 | 0.8872 | | 0.0186 | 6.0100 | 5096 | 0.7927 | 0.8107 | | 0.0017 | 7.0100 | 5824 | 0.7084 | 0.8123 | | 0.0003 | 8.0100 | 6552 | 0.7571 | 0.8560 | | 0.003 | 9.0100 | 7280 | 0.4314 | 0.8988 | | 0.0004 | 10.0100 | 8008 | 0.9204 | 0.8165 | | 0.0002 | 11.0100 | 8736 | 1.2702 | 0.7901 | | 0.0002 | 12.0100 | 9464 | 0.5825 | 0.8831 | | 0.0004 | 13.0100 | 10192 | 0.9033 | 0.8288 | | 0.0002 | 14.0100 | 10920 | 0.9259 | 0.8527 | | 0.0051 | 15.0100 | 11648 | 1.0172 | 0.8337 | | 0.0002 | 16.0100 | 12376 | 1.0718 | 0.8214 | | 0.0 | 17.0100 | 13104 | 1.0214 | 0.8444 | ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0

Source context: 6 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0328_final_253259