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Videomae Base Finetuned Kinetics Allkisa Crop Background 0312 Clip Duration Abnormal09

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: 0.1607 Accuracy: 0.9361

KindOtherBase modelMCG-NJU/videomae-base-finetuned-kineticsVersionRevision d2da739

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base-finetuned-kinetics
Version
vRevision d2da739
Updated
Sep 18, 2026
Downloads
3
Source
Hugging Face

Source description

videomae-base-finetuned-kinetics-allkisa-crop-background-0312-clip_duration-abnormal09

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: 0.1607
  • Accuracy: 0.9361

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: 8
  • eval_batch_size: 8
  • seed: 42
  • 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: 0.05
  • training_steps: 31000

Training results

Training LossEpochStepValidation LossAccuracy
0.00530.01003110.33180.8869
0.00441.01006220.41470.8724
0.02982.01009330.38030.8562
1.12953.010012440.34650.8740
0.25174.010015550.36690.8578
0.98085.010018660.35890.8691
0.00246.010021770.50940.8401
0.00137.010024880.46060.8708
0.00278.010027990.50180.8255
0.70389.010031100.64410.8288
0.61610.010034210.50860.8627

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0

Sources

1 source

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 855f43c6ddf0…5fd3 · Hugging Face

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Source excerpts

2 excerpts

--- 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-base-finetuned-kinetics-allkisa-crop-background-0312-clip_duration-abnormal09 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-finetuned-kinetics-allkisa-crop-background-0312-clip_duration-abnormal09 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: 0.1607 - Accuracy: 0.9361 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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: 0.05 - training_steps: 31000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.0053 | 0.0100 | 311 | 0.3318 | 0.8869 | | 0.0044 | 1.0100 | 622 | 0.4147 | 0.8724 | | 0.0298 | 2.0100 | 933 | 0.3803 | 0.8562 | | 1.1295 | 3.0100 | 1244 | 0.3465 | 0.8740 | | 0.2517 | 4.0100 | 1555 | 0.3669 | 0.8578 | | 0.9808 | 5.0100 | 1866 | 0.3589 | 0.8691 | | 0.0024 | 6.0100 | 2177 | 0.5094 | 0.8401 | | 0.0013 | 7.0100 | 2488 | 0.4606 | 0.8708 | | 0.0027 | 8.0100 | 2799 | 0.5018 | 0.8255 | | 0.7038 | 9.0100 | 3110 | 0.6441 | 0.8288 | | 0.616 | 10.0100 | 3421 | 0.5086 | 0.8627 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

Source context: 3 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-allkisa-crop-background-0312-clip_duration-abnormal09