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VideoMAE Base Wlasl 100 Signer 200ep CoR

This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: Loss: 3.6367 Accuracy: 0.4793

Sourcehuggingface.co/Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR/tree/49511f5a5046b7cb05be3feb4648e9901dc541d2
KindOtherBase modelMCG-NJU/videomae-baseVersionRevision 49511f5Bgrade

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base
Version
vRevision 49511f5
Updated
Oct 24, 2026
Downloads
10
Source
Hugging Face

Source description

VideoMAE_base_wlasl_100__signer_200ep_coR

This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 3.6367
  • Accuracy: 0.4793

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: 36000
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracy
18.66630.0051804.65850.0118
18.59851.00503604.63450.0207
18.50622.00505404.63010.0148
18.34823.00507214.62120.0178
18.39634.0059014.61820.0207
18.29795.005010814.60920.0237
18.20536.005012614.65050.0178
18.02847.005014424.58780.0266
17.97748.00516224.58750.0325
17.6729.005018024.48920.0355
17.074610.005019824.30390.0503
16.293911.005021634.12990.0562
15.275512.00523433.88150.1006
13.84313.005025233.71650.1213
12.415314.005027033.45090.1657
10.980915.005028843.17550.2249
9.577516.00530642.94890.2544
8.415117.005032442.82760.3195
7.053118.005034242.67190.3225
5.979519.005036052.61430.3491
4.846920.00537852.60370.3521
3.938421.005039652.40710.3935
3.17622.005041452.34980.4260
2.553323.005043262.25460.4290
2.021724.00545062.26160.4408
1.503225.005046862.29620.4290
1.305226.005048662.36130.4024
1.022327.005050472.34790.4290
0.863828.00552272.28870.4556
0.762329.005054072.31400.4497
0.68830.005055872.58850.4201
0.490531.005057682.43100.4527
0.446632.00559482.41340.4556
0.429133.005061282.31130.4734
0.328634.005063082.62190.4704
0.331435.005064892.64440.4556
0.272436.00566692.61870.4704
0.312637.005068492.88260.4438
0.267738.005070292.70600.5059
0.161639.005072102.72370.5030
0.265740.00573902.80890.4822
0.264141.005075702.88140.4941
0.098842.005077503.01110.5059
0.141243.005079313.07380.4763
0.213244.00581113.11860.4704
0.232245.005082913.16730.4734
0.213546.005084713.06420.4793
0.268947.005086523.23370.4615
0.215748.00588323.23570.4793
0.22949.005090122.97040.5237
0.205150.005091923.14820.4763
0.208351.005093733.41350.4615
0.229852.00595533.31650.5
0.133853.005097333.11290.4852
0.147654.005099133.28210.4734
0.220655.0050100943.16880.5178
0.169856.005102743.34870.4911
0.156857.0050104543.09340.5059
0.105958.0050106343.69090.4763
0.137759.0050108153.46590.4882
0.19460.005109953.87660.4231
0.32361.0050111753.76570.4438
0.211662.0050113553.58750.4911
0.322163.0050115363.62620.4675
0.212364.005117163.25480.4852
0.148565.0050118963.23150.5266
0.137266.0050120763.71620.4793
0.130367.0050122573.57590.4911
0.153268.005124373.42570.4882
0.134569.0050126173.68850.4645
0.218270.0050127973.81180.4734
0.211871.0050129783.82030.5030
0.193172.005131583.85470.4970
0.184973.0050133383.50800.5237
0.215174.0050135184.12610.4527
0.214475.0050136993.47030.5118
0.209376.005138793.59650.4911
0.117177.0050140593.60710.5118
0.137778.0050142393.64240.4970
0.165879.0050144203.78430.4793
0.254380.005146003.74430.4970
0.184981.0050147803.91820.4941
0.116882.0050149603.98520.4497
0.140683.0050151413.78110.4970
0.160884.005153213.81680.4793
0.134885.0050155013.63670.4793

Framework versions

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

Sources

1 source
Hugging FacePrimaryVersion 49511f5a5046b7cb05be3feb4648e9901dc541d2
huggingface.co/Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR/tree/49511f5a5046b7cb05be3feb4648e9901dc541d2

Verified Oct 24

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 c2f495587666…06b3 · Hugging Face

Download file

Source excerpts

3 excerpts
ReadmeAug 6huggingface.co/Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: VideoMAE_base_wlasl_100__signer_200ep_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. --> # VideoMAE_base_wlasl_100__signer_200ep_coR This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.6367 - Accuracy: 0.4793 ## 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: 36000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:-----:|:---------------:|:--------:| | 18.6663 | 0.005 | 180 | 4.6585 | 0.0118 | | 18.5985 | 1.0050 | 360 | 4.6345 | 0.0207 | | 18.5062 | 2.0050 | 540 | 4.6301 | 0.0148 | | 18.3482 | 3.0050 | 721 | 4.6212 | 0.0178 | | 18.3963 | 4.005 | 901 | 4.6182 | 0.0207 | | 18.2979 | 5.0050 | 1081 | 4.6092 | 0.0237 | | 18.2053 | 6.0050 | 1261 | 4.6505 | 0.0178 | | 18.0284 | 7.0050 | 1442 | 4.5878 | 0.0266 | | 17.9774 | 8.005 | 1622 | 4.5875 | 0.0325 | | 17.672 | 9.0050 | 1802 | 4.4892 | 0.0355 | | 17.0746 | 10.0050 | 1982 | 4.3039 | 0.0503 | | 16.2939 | 11.0050 | 2163 | 4.1299 | 0.0562 | | 15.2755 | 12.005 | 2343 | 3.8815 | 0.1006 | | 13.843 | 13.0050 | 2523 | 3.7165 | 0.1213 | | 12.4153 | 14.0050 | 2703 | 3.4509 | 0.1657 | | 10.9809 | 15.0050 | 2884 | 3.1755 | 0.2249 | | 9.5775 | 16.005 | 3064 | 2.9489 | 0.2544 | | 8.4151 | 17.0050 | 3244 | 2.8276 | 0.3195 | | 7.0531 | 18.0050 | 3424 | 2.6719 | 0.3225 | | 5.9795 | 19.0050 | 3605 | 2.6143 | 0.3491 | |...

NoteOct 24huggingface.co/Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR

Source context: 10 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR

NoteAug 6huggingface.co/Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR

Source context: 6 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_base_wlasl_100__signer_200ep_coR