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Videomae Base Finetuned Kinetics 0403 Final 5sec

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: 0.2477 Accuracy: 0.9053

Sourcehuggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0403_final_5sec/tree/d1f062c5f2690699e83d17288bf476f0f0d0b445
KindOtherBase modelMCG-NJU/videomae-base-finetuned-kineticsVersionRevision d1f062c

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base-finetuned-kinetics
Version
vRevision d1f062c
Updated
Sep 18, 2026
Downloads
2
Source
Hugging Face

Source description

videomae-base-finetuned-kinetics-0403_final_5sec

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: 0.2477
  • Accuracy: 0.9053

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: 64400

Training results

Training LossEpochStepValidation LossAccuracy
0.15320.01006450.22120.9151
0.00511.010012900.86800.7644
0.26252.010019350.45530.8616
0.01553.010025800.46820.8643
0.0214.010032250.62840.8196
0.64065.010038700.39390.9116
1.04176.010045151.15310.7898
0.00277.010051600.73260.8152
0.00178.010058050.57270.8809
0.00099.010064500.49520.8949
0.007710.010070950.73020.8371
0.001111.010077400.83040.8100
0.000212.010083850.85390.8135
0.000213.010090301.46900.7434
0.948414.010096750.78600.8319
0.014515.0100103201.35280.7758

Framework versions

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

Sources

1 source
Hugging FacePrimaryVersion d1f062c5f2690699e83d17288bf476f0f0d0b445
huggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0403_final_5sec/tree/d1f062c5f2690699e83d17288bf476f0f0d0b445

Verified Sep 18

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 80c72a1dfa59…1c7b · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 18huggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0403_final_5sec

--- 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-0403_final_5sec 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-0403_final_5sec 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: 0.2477 - Accuracy: 0.9053 ## 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: 64400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:-----:|:---------------:|:--------:| | 0.1532 | 0.0100 | 645 | 0.2212 | 0.9151 | | 0.0051 | 1.0100 | 1290 | 0.8680 | 0.7644 | | 0.2625 | 2.0100 | 1935 | 0.4553 | 0.8616 | | 0.0155 | 3.0100 | 2580 | 0.4682 | 0.8643 | | 0.021 | 4.0100 | 3225 | 0.6284 | 0.8196 | | 0.6406 | 5.0100 | 3870 | 0.3939 | 0.9116 | | 1.0417 | 6.0100 | 4515 | 1.1531 | 0.7898 | | 0.0027 | 7.0100 | 5160 | 0.7326 | 0.8152 | | 0.0017 | 8.0100 | 5805 | 0.5727 | 0.8809 | | 0.0009 | 9.0100 | 6450 | 0.4952 | 0.8949 | | 0.0077 | 10.0100 | 7095 | 0.7302 | 0.8371 | | 0.0011 | 11.0100 | 7740 | 0.8304 | 0.8100 | | 0.0002 | 12.0100 | 8385 | 0.8539 | 0.8135 | | 0.0002 | 13.0100 | 9030 | 1.4690 | 0.7434 | | 0.9484 | 14.0100 | 9675 | 0.7860 | 0.8319 | | 0.0145 | 15.0100 | 10320 | 1.3528 | 0.7758 | ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0

NoteSep 18huggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0403_final_5sec

Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0403_final_5sec