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Videomae Base Finetuned Kinetics 0320 Abnormal Val02 Pretest

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: 0.3466 Accuracy: 0.9361

Fuentehuggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0320-abnormal_val02_pretest/tree/cb985b0d742aed735d8db4233c172deb3a7b7f8f
TipoOtherModelo baseMCG-NJU/videomae-base-finetuned-kineticsVersiónRevision cb985b0

Fuente del modelo

Tipo
Other
Tamaño del archivo
329 MB
Modelo base
MCG-NJU/videomae-base-finetuned-kinetics
Versión
vRevision cb985b0
Actualizado
18 sept 2026
Descargas
6
Fuente
Hugging Face

Descripción de la fuente

videomae-base-finetuned-kinetics-0320-abnormal_val02_pretest

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: 0.3466
  • Accuracy: 0.9361

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: 71400

Training results

Training LossEpochStepValidation LossAccuracy
0.0680.01007150.32750.8850
0.021.010014300.25330.9027
0.00492.010021450.30370.8947
0.88963.010028600.27050.9148
0.58954.010035750.30390.8955
0.00075.010042900.29030.9124
0.00036.010050050.31060.9068
0.00157.010057200.31860.9027
0.00038.010064350.45630.8818
0.11859.010071500.35320.9092
0.000410.010078650.40580.9003
0.136811.010085800.31170.9220
0.001312.010092950.32170.9156
0.000113.0100100100.36350.9164
0.001714.0100107250.39650.9124
0.000215.0100114400.36490.9260
0.000616.0100121550.53600.8915
0.000117.0100128700.42250.9132
0.000118.0100135850.36390.9260
0.000119.0100143000.37820.9268
0.000220.0100150150.39940.9156
0.021.0100157300.39310.9228
0.000222.0100164450.44160.9148
0.364523.0100171600.46890.9172
0.000124.0100178750.41370.9164
0.000125.0100185900.37430.9260
0.146426.0100193050.34040.9325
0.027.0100200200.40100.9220
0.028.0100207350.40680.9196
0.000129.0100214500.35570.9317
0.000130.0100221650.38540.9268
0.043931.0100228800.41070.9236
0.000132.0100235950.36660.9309
0.033.0100243100.35880.9309
0.000134.0100250250.36350.9317
0.000135.0100257400.36910.9293
0.036.0100264550.37550.9293

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0

Fuentes

1 fuente
Hugging FacePrincipalVersión cb985b0d742aed735d8db4233c172deb3a7b7f8f
huggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0320-abnormal_val02_pretest/tree/cb985b0d742aed735d8db4233c172deb3a7b7f8f

Verificado 18 sept

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 329 MB · SHA-256 07749b948e18…c928 · Hugging Face

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Extractos de fuentes

2 extractos
Readme18 septhuggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0320-abnormal_val02_pretest

--- 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-0320-abnormal_val02_pretest 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-0320-abnormal_val02_pretest 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: 0.3466 - Accuracy: 0.9361 ## 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: 71400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:-----:|:---------------:|:--------:| | 0.068 | 0.0100 | 715 | 0.3275 | 0.8850 | | 0.02 | 1.0100 | 1430 | 0.2533 | 0.9027 | | 0.0049 | 2.0100 | 2145 | 0.3037 | 0.8947 | | 0.8896 | 3.0100 | 2860 | 0.2705 | 0.9148 | | 0.5895 | 4.0100 | 3575 | 0.3039 | 0.8955 | | 0.0007 | 5.0100 | 4290 | 0.2903 | 0.9124 | | 0.0003 | 6.0100 | 5005 | 0.3106 | 0.9068 | | 0.0015 | 7.0100 | 5720 | 0.3186 | 0.9027 | | 0.0003 | 8.0100 | 6435 | 0.4563 | 0.8818 | | 0.1185 | 9.0100 | 7150 | 0.3532 | 0.9092 | | 0.0004 | 10.0100 | 7865 | 0.4058 | 0.9003 | | 0.1368 | 11.0100 | 8580 | 0.3117 | 0.9220 | | 0.0013 | 12.0100 | 9295 | 0.3217 | 0.9156 | | 0.0001 | 13.0100 | 10010 | 0.3635 | 0.9164 | | 0.0017 | 14.0100 | 10725 | 0.3965 | 0.9124 | | 0.0002 | 15.0100 | 11440 | 0.3649 | 0.9260 | | 0.0006 | 16.0100 | 12155 | 0.5360 | 0.8915 | | 0.0001 | 17.0100 | 12870 | 0.4225 | 0.9132 | | 0.0001 | 18.0100 | 13585 | 0.3639 | 0.9260 | | 0.0001 | 19.0100 | 14300 | 0.3782 | 0.9268 | | 0.00...

Note18 septhuggingface.co/d2o2ji/videomae-base-finetuned-kinetics-0320-abnormal_val02_pretest

Source context: 6 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0320-abnormal_val02_pretest