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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

Quellehuggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep/tree/8e4d635b09cffd39603e5c9cbc1fc1d464aa556f
ArtOtherBasismodellMCG-NJU/videomae-base-finetuned-kineticsVersionRevision 8e4d635CBewertung

Modellquelle

Art
Other
Dateigröße
329 MB
Basismodell
MCG-NJU/videomae-base-finetuned-kinetics
Version
vRevision 8e4d635
Aktualisiert
24. Okt. 2026
Downloads
7
Quelle
Hugging Face

Quellenbeschreibung

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

Quellen

1 Quelle
Hugging FacePrimärVersion 8e4d635b09cffd39603e5c9cbc1fc1d464aa556f
huggingface.co/Shawon16/videoMAE_kinetics_wlasl100_coR_30ep/tree/8e4d635b09cffd39603e5c9cbc1fc1d464aa556f

Verifiziert 24. Okt.

Modellartefakte

1 Artefakt

model.safetensors

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

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Quellenauszüge

3 Auszüge
Readme6. Aug.huggingface.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. Okt.huggingface.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. Aug.huggingface.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