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VideoMAE Kinetics Fold 8 BdSLW60 SKF

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: 0.0138 Accuracy: 0.9975

TipoModeloModelo baseMCG-NJU/videomae-base-finetuned-kineticsVersãoRevision e9cf9c1Bnota

Fonte do modelo

Tipo
Modelo
Tamanho do arquivo
329 MB
Modelo base
MCG-NJU/videomae-base-finetuned-kinetics
Versão
vRevision e9cf9c1
Atualizado
30 de out. de 2026
Downloads
4
Fonte
Hugging Face

Descrição da fonte

VideoMAE_Kinetics_fold__8__BdSLW60_SKF

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: 0.0138
  • Accuracy: 0.9975

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • 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: 0.1
  • training_steps: 9030
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracy
8.26290.19030.27820.9589
0.37261.100118070.05540.9863
0.09632.127100.04550.9913
0.05193.100136140.01830.9950
0.034.145170.01470.9963
0.03225.100154210.00860.9963
0.01276.163240.00700.9975
0.017.100172280.01580.9938
0.00218.181310.03560.9950
0.0019.099590300.01380.9975

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.1

Fontes

1 fonte
Hugging FacePrimárioVersão e9cf9c12c23fb1989e8747c2e41c44318388cd80
huggingface.co/Shawon16/VideoMAE_Kinetics_fold__8__BdSLW60_SKF/tree/e9cf9c12c23fb1989e8747c2e41c44318388cd80

Verificado 30 de out.

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Artefatos de modelo

2 artefatos

checkpoint/model.safetensors

safetensors · FP32 (tensor header) · 329 MB · SHA-256 b9c9db4094d1…b641 · Hugging Face

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model.safetensors

safetensors · FP32 (tensor header) · 329 MB · SHA-256 b9c9db4094d1…b641 · Hugging Face

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Trechos de fonte

3 trechos

--- 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_Kinetics_fold__8__BdSLW60_SKF 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_Kinetics_fold__8__BdSLW60_SKF 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: 0.0138 - Accuracy: 0.9975 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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: 0.1 - training_steps: 9030 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 8.2629 | 0.1 | 903 | 0.2782 | 0.9589 | | 0.3726 | 1.1001 | 1807 | 0.0554 | 0.9863 | | 0.0963 | 2.1 | 2710 | 0.0455 | 0.9913 | | 0.0519 | 3.1001 | 3614 | 0.0183 | 0.9950 | | 0.03 | 4.1 | 4517 | 0.0147 | 0.9963 | | 0.0322 | 5.1001 | 5421 | 0.0086 | 0.9963 | | 0.0127 | 6.1 | 6324 | 0.0070 | 0.9975 | | 0.01 | 7.1001 | 7228 | 0.0158 | 0.9938 | | 0.0021 | 8.1 | 8131 | 0.0356 | 0.9950 | | 0.001 | 9.0995 | 9030 | 0.0138 | 0.9975 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1

Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_Kinetics_fold__8__BdSLW60_SKF

Source context: 25 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_Kinetics_fold__8__BdSLW60_SKF