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Unlearn Ucf101 Videomae Base Random Label 2 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: 0.9465 Accuracy: 0.7882 Test Accuracy: 0.7882 Df Accuracy: 0.9062...

KindOtherBase modelMCG-NJU/videomae-baseVersionRevision adac336Bgrade

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base
Version
vRevision adac336
Updated
Oct 4, 2026
Downloads
5
Source
Hugging Face

Source description

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: 0.9465
  • Accuracy: 0.7882
  • Test Accuracy: 0.7882
  • Df Accuracy: 0.9062
  • Unlearn Overall Accuracy: 0.4410
  • Unlearn Time: 4124.0536

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: 3

Training results

Training LossEpochStepValidation LossAccuracyOverall AccuracyUnlearn Overall AccuracyTime
No log1.013001.05360.92960.41640.4164-1
No log2.016000.94650.90620.44100.4410-1
No log2.998940.82740.95310.43110.4311-1

Framework versions

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

Sources

1 source
Hugging FacePrimaryVersion adac3369640266822f6e2d585469f5268602439a
huggingface.co/jialicheng/unlearn_ucf101_videomae-base_random_label_2_42/tree/adac3369640266822f6e2d585469f5268602439a

Verified Aug 21

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 0cd04158bde6…9d14 · Hugging Face

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Source excerpts

2 excerpts

--- 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: 0.9465 - Accuracy: 0.7882 - Test Accuracy: 0.7882 - Df Accuracy: 0.9062 - Unlearn Overall Accuracy: 0.4410 - Unlearn Time: 4124.0536 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Overall Accuracy | Unlearn Overall Accuracy | Time | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:------------------------:|:----:| | No log | 1.01 | 300 | 1.0536 | 0.9296 | 0.4164 | 0.4164 | -1 | | No log | 2.01 | 600 | 0.9465 | 0.9062 | 0.4410 | 0.4410 | -1 | | No log | 2.99 | 894 | 0.8274 | 0.9531 | 0.4311 | 0.4311 | -1 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2

Source context: 5 downloads · 0 likes · Pipeline video-classification · Repo jialicheng/unlearn_ucf101_videomae-base_random_label_2_42