Shawon16 / ViViT_LSA64_SR_6

huggingface.co
Total runs: 3
24-hour runs: 0
7-day runs: -1
30-day runs: 0
Model's Last Updated: January 04 2025
video-classification

Introduction of ViViT_LSA64_SR_6

Model Details of ViViT_LSA64_SR_6

ViViT_LSA64_SR_6

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: 0.0190
  • Accuracy: 0.9961
  • Precision: 0.9969
  • Recall: 0.9961
  • F1: 0.9960
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: 8640
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
15.7817 0.0333 288 2.8824 0.4609 0.5309 0.4609 0.4307
4.7558 1.0333 576 0.5582 0.9492 0.9586 0.9492 0.9470
0.5173 2.0333 864 0.0999 0.9805 0.9854 0.9805 0.9798
0.1244 3.0333 1152 0.0102 1.0 1.0 1.0 1.0
0.0043 4.0333 1440 0.0265 0.9922 0.9938 0.9922 0.9921
0.021 5.0333 1728 0.0200 0.9922 0.9938 0.9922 0.9921
0.0014 6.0333 2016 0.0012 1.0 1.0 1.0 1.0
0.0414 7.0333 2304 0.0075 0.9961 0.9969 0.9961 0.9960
0.0386 8.0333 2592 0.0190 0.9961 0.9969 0.9961 0.9960
Framework versions
  • Transformers 4.46.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.1

Runs of Shawon16 ViViT_LSA64_SR_6 on huggingface.co

3
Total runs
0
24-hour runs
0
3-day runs
-1
7-day runs
0
30-day runs

More Information About ViViT_LSA64_SR_6 huggingface.co Model

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ViViT_LSA64_SR_6 install

ViViT_LSA64_SR_6 is an open source model from GitHub that offers a free installation service, and any user can find ViViT_LSA64_SR_6 on GitHub to install. At the same time, huggingface.co provides the effect of ViViT_LSA64_SR_6 install, users can directly use ViViT_LSA64_SR_6 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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