Saltar al contenido
ModelPublicadoExterno
Descargar

ViViT Wlasl 100 Signer 20ep CoR

This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set: Loss: 1.2104 Accuracy: 0.6864

Fuentehuggingface.co/Shawon16/ViViT_wlasl_100__signer_20ep_coR_/tree/0b90c75f6d3f4b47993a838325026e4082e6579f
TipoOtherModelo basegoogle/vivit-b-16x2-kinetics400VersiónRevision 0b90c75Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
338 MB
Modelo base
google/vivit-b-16x2-kinetics400
Versión
vRevision 0b90c75
Actualizado
24 oct 2026
Descargas
3
Fuente
Hugging Face

Descripción de la fuente

ViViT_wlasl_100__signer_20ep_coR_

This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2104
  • Accuracy: 0.6864

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: 3600
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracy
18.67830.051804.54030.0414
16.8511.04993603.89170.1568
12.36492.04995403.00260.3284
7.8773.05017212.39640.4852
4.85274.059012.01900.5651
2.75995.049910811.79040.5976
1.5166.049912611.57630.6272
0.83437.050114421.46780.6154
0.41448.0516221.42050.6420
0.26249.049918021.28960.6716
0.149810.049919821.27340.6627
0.090611.050121631.23070.6805
0.077912.0523431.28180.6805
0.064713.049925231.21840.6893
0.061514.049927031.22040.6953
0.051315.050128841.21550.6716
0.045616.0530641.20520.6982
0.021917.049932441.21220.6864
0.03318.049934241.21590.6893
0.020619.048736001.21040.6864

Framework versions

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

Fuentes

1 fuente
Hugging FacePrincipalVersión 0b90c75f6d3f4b47993a838325026e4082e6579f
huggingface.co/Shawon16/ViViT_wlasl_100__signer_20ep_coR_/tree/0b90c75f6d3f4b47993a838325026e4082e6579f

Verificado 24 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 338 MB · SHA-256 364dd47bdd28…8d15 · Hugging Face

Download file

Extractos de fuentes

3 extractos
Readme6 agohuggingface.co/Shawon16/ViViT_wlasl_100__signer_20ep_coR_

--- library_name: transformers license: mit base_model: google/vivit-b-16x2-kinetics400 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ViViT_wlasl_100__signer_20ep_coR_ 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. --> # ViViT_wlasl_100__signer_20ep_coR_ This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2104 - Accuracy: 0.6864 ## 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: 3600 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 18.6783 | 0.05 | 180 | 4.5403 | 0.0414 | | 16.851 | 1.0499 | 360 | 3.8917 | 0.1568 | | 12.3649 | 2.0499 | 540 | 3.0026 | 0.3284 | | 7.877 | 3.0501 | 721 | 2.3964 | 0.4852 | | 4.8527 | 4.05 | 901 | 2.0190 | 0.5651 | | 2.7599 | 5.0499 | 1081 | 1.7904 | 0.5976 | | 1.516 | 6.0499 | 1261 | 1.5763 | 0.6272 | | 0.8343 | 7.0501 | 1442 | 1.4678 | 0.6154 | | 0.4144 | 8.05 | 1622 | 1.4205 | 0.6420 | | 0.2624 | 9.0499 | 1802 | 1.2896 | 0.6716 | | 0.1498 | 10.0499 | 1982 | 1.2734 | 0.6627 | | 0.0906 | 11.0501 | 2163 | 1.2307 | 0.6805 | | 0.0779 | 12.05 | 2343 | 1.2818 | 0.6805 | | 0.0647 | 13.0499 | 2523 | 1.2184 | 0.6893 | | 0.0615 | 14.0499 | 2703 | 1.2204 | 0.6953 | | 0.0513 | 15.0501 | 2884 | 1.2155 | 0.6716 | | 0.0456 | 16.05 | 3064 | 1.2052 | 0.6982 | | 0.0219 | 17.0499 | 3244 | 1.2122 | 0.6864 | | 0.033 | 18.0499 | 3424 | 1.2159 | 0.6893 | | 0.0206 | 19.0487 | 3600 | 1.2104 | 0.6864 | ### Framework ver...

Note24 octhuggingface.co/Shawon16/ViViT_wlasl_100__signer_20ep_coR_

Source context: 3 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/ViViT_wlasl_100__signer_20ep_coR_

Note6 agohuggingface.co/Shawon16/ViViT_wlasl_100__signer_20ep_coR_

Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/ViViT_wlasl_100__signer_20ep_coR_