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ViViT BdSLW60 FrameRate Corrected Without Augment 20 Epch

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: 0.6841 Accuracy: 0.86 Precision: 0.8823 Recall: 0.86 F1: 0.8452

Sourcehuggingface.co/Shawon16/ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch/tree/20017d50fcffe4344bc890a0dd7819c61021bc7a
TypeOtherModèle de basegoogle/vivit-b-16x2-kinetics400VersionRevision 20017d5

Source du modèle

Type
Other
Taille du fichier
338 MB
Modèle de base
google/vivit-b-16x2-kinetics400
Version
vRevision 20017d5
Mis à jour
18 sept. 2026
Téléchargements
4
Source
Hugging Face

Description de la source

ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch

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: 0.6841
  • Accuracy: 0.86
  • Precision: 0.8823
  • Recall: 0.86
  • F1: 0.8452

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: 18560
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracyPrecisionRecallF1
3.11240.05019291.51550.7050.75290.7050.6760
0.12161.050118580.65760.81670.81970.81670.7920
0.00482.050127870.46300.860.88680.860.8338
0.00863.050137160.43050.87330.89270.87330.8600
0.25274.050146451.07630.7550.81360.7550.7172
0.06815.050155740.78360.82830.82020.82830.8002
0.02566.050165030.71970.83330.86670.83330.8153
0.0647.050174320.69180.85330.87300.85330.8374
0.00018.050183610.68410.860.88230.860.8452

Framework versions

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

Sources

1 source
Hugging FacePrincipalVersion 20017d50fcffe4344bc890a0dd7819c61021bc7a
huggingface.co/Shawon16/ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch/tree/20017d50fcffe4344bc890a0dd7819c61021bc7a

Vérifié 18 sept.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 338 MB · SHA-256 7ffbb250792b…68c4 · Hugging Face

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Extraits de sources

2 extraits
Readme18 sept.huggingface.co/Shawon16/ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch

--- library_name: transformers license: mit base_model: google/vivit-b-16x2-kinetics400 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch 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_BdSLW60_FrameRate_Corrected_without_Augment_20_epch 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: 0.6841 - Accuracy: 0.86 - Precision: 0.8823 - Recall: 0.86 - F1: 0.8452 ## 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: 18560 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 3.1124 | 0.0501 | 929 | 1.5155 | 0.705 | 0.7529 | 0.705 | 0.6760 | | 0.1216 | 1.0501 | 1858 | 0.6576 | 0.8167 | 0.8197 | 0.8167 | 0.7920 | | 0.0048 | 2.0501 | 2787 | 0.4630 | 0.86 | 0.8868 | 0.86 | 0.8338 | | 0.0086 | 3.0501 | 3716 | 0.4305 | 0.8733 | 0.8927 | 0.8733 | 0.8600 | | 0.2527 | 4.0501 | 4645 | 1.0763 | 0.755 | 0.8136 | 0.755 | 0.7172 | | 0.0681 | 5.0501 | 5574 | 0.7836 | 0.8283 | 0.8202 | 0.8283 | 0.8002 | | 0.0256 | 6.0501 | 6503 | 0.7197 | 0.8333 | 0.8667 | 0.8333 | 0.8153 | | 0.064 | 7.0501 | 7432 | 0.6918 | 0.8533 | 0.8730 | 0.8533 | 0.8374 | | 0.0001 | 8.0501 | 8361 | 0.6841 | 0.86 | 0.8823 | 0.86 | 0.8452 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1

Note18 sept.huggingface.co/Shawon16/ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch

Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch