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Unlearn Ucf101 Videomae Base Scrub 4 42

This model is a fine-tuned version of MCG-NJU/videomae-base on the ucf101 dataset. It achieves the following results on the evaluation set: Loss: 0.7989 Accuracy: 0.8310 Test Accuracy: 0.8310 Df Accuracy: 0.9915...

TipoOtherModelo baseMCG-NJU/videomae-baseVersãoRevision 7601593Cnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
329 MB
Modelo base
MCG-NJU/videomae-base
Versão
vRevision 7601593
Atualizado
7 de out. de 2026
Downloads
2
Fonte
Hugging Face

Descrição da fonte

ucf101_42

This model is a fine-tuned version of MCG-NJU/videomae-base on the ucf101 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7989
  • Accuracy: 0.8310
  • Test Accuracy: 0.8310
  • Df Accuracy: 0.9915
  • Unlearn Overall Accuracy: 0.4197
  • Unlearn Time: 3653.1632

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: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1.5

Training results

Training LossEpochStepValidation LossAccuracyOverall AccuracyUnlearn Overall AccuracyTime
No log1.012880.79390.99320.41740.4174-1
No log1.494280.79890.99150.41970.4197-1

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.2+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.2

Fontes

1 fonte
Hugging FacePrimárioVersão 760159345eef77b15ced67e5fdd1a0b3a78631c9
huggingface.co/jialicheng/unlearn_ucf101_videomae-base_scrub_4_42/tree/760159345eef77b15ced67e5fdd1a0b3a78631c9

Verificado 21 de ago.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 329 MB · SHA-256 ffe11fd43dfb…0891 · Hugging Face

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Trechos de fonte

2 trechos

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - video-classification - generated_from_trainer metrics: - accuracy model-index: - name: ucf101_42 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. --> # ucf101_42 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on the ucf101 dataset. It achieves the following results on the evaluation set: - Loss: 0.7989 - Accuracy: 0.8310 - Test Accuracy: 0.8310 - Df Accuracy: 0.9915 - Unlearn Overall Accuracy: 0.4197 - Unlearn Time: 3653.1632 ## 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: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Overall Accuracy | Unlearn Overall Accuracy | Time | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:------------------------:|:----:| | No log | 1.01 | 288 | 0.7939 | 0.9932 | 0.4174 | 0.4174 | -1 | | No log | 1.49 | 428 | 0.7989 | 0.9915 | 0.4197 | 0.4197 | -1 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2

Source context: 2 downloads · 0 likes · Pipeline video-classification · Repo jialicheng/unlearn_ucf101_videomae-base_scrub_4_42