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VideoMAE Kinetics Wlasl100 CoR 30ep

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: 0.9436 Accuracy: 0.7692

Fuentehuggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep/tree/8e4d635b09cffd39603e5c9cbc1fc1d464aa556f
TipoOtherModelo baseMCG-NJU/videomae-base-finetuned-kineticsVersiónRevision 8e4d635Ccalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
329 MB
Modelo base
MCG-NJU/videomae-base-finetuned-kinetics
Versión
vRevision 8e4d635
Actualizado
24 oct 2026
Descargas
7
Fuente
Hugging Face

Descripción de la fuente

videoMAE_kinetics_wlasl100_coR_30ep

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: 0.9436
  • Accuracy: 0.7692

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: 5400
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracy
18.48120.03331804.57350.0325
17.44881.03333603.98650.1272
13.73912.03325403.14020.4024
9.49913.03347212.36360.5444
6.2924.03339011.91270.6213
3.9935.033310811.56340.6923
2.4526.033212611.41930.6893
1.48817.033414421.28400.7101
0.90368.033316221.16050.7426
0.52819.033318021.12990.7101
0.322810.033219821.12830.7189
0.185411.033421630.95300.7426
0.130412.033323431.03240.7604
0.079713.033325230.99850.7456
0.0714.033227030.99050.7604
0.068115.033428840.96330.7574
0.055916.033330640.91130.7692
0.054817.033332440.92720.7544
0.036718.033234240.95050.7692
0.063619.033436050.92780.7633
0.076720.033337850.93330.7604
0.07121.033339650.94360.7692

Framework versions

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

Fuentes

1 fuente
Hugging FacePrincipalVersión 8e4d635b09cffd39603e5c9cbc1fc1d464aa556f
huggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep/tree/8e4d635b09cffd39603e5c9cbc1fc1d464aa556f

Verificado 24 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 329 MB · SHA-256 4c84a1678c84…3718 · Hugging Face

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Extractos de fuentes

3 extractos
Readme6 agohuggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep

--- 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_wlasl100_coR_30ep 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_wlasl100_coR_30ep 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: 0.9436 - Accuracy: 0.7692 ## 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: 5400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 18.4812 | 0.0333 | 180 | 4.5735 | 0.0325 | | 17.4488 | 1.0333 | 360 | 3.9865 | 0.1272 | | 13.7391 | 2.0332 | 540 | 3.1402 | 0.4024 | | 9.4991 | 3.0334 | 721 | 2.3636 | 0.5444 | | 6.292 | 4.0333 | 901 | 1.9127 | 0.6213 | | 3.993 | 5.0333 | 1081 | 1.5634 | 0.6923 | | 2.452 | 6.0332 | 1261 | 1.4193 | 0.6893 | | 1.4881 | 7.0334 | 1442 | 1.2840 | 0.7101 | | 0.9036 | 8.0333 | 1622 | 1.1605 | 0.7426 | | 0.5281 | 9.0333 | 1802 | 1.1299 | 0.7101 | | 0.3228 | 10.0332 | 1982 | 1.1283 | 0.7189 | | 0.1854 | 11.0334 | 2163 | 0.9530 | 0.7426 | | 0.1304 | 12.0333 | 2343 | 1.0324 | 0.7604 | | 0.0797 | 13.0333 | 2523 | 0.9985 | 0.7456 | | 0.07 | 14.0332 | 2703 | 0.9905 | 0.7604 | | 0.0681 | 15.0334 | 2884 | 0.9633 | 0.7574 | | 0.0559 | 16.0333 | 3064 | 0.9113 | 0.7692 | | 0.0548 | 17.0333 | 3244 | 0.9272 | 0.7544 | | 0.0367 | 18.0332 | 3424 | 0.9505 | 0.7692 | | 0.0636 | 19.0...

Note24 octhuggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep

Source context: 7 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/videoMAE_kinetics_wlasl100_coR_30ep

Note6 agohuggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep

Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/videoMAE_kinetics_wlasl100_coR_30ep