Ir para o conteúdo
ModelListadosExterno
Baixar

Videomae Base Finetuned Kinetics Finetuned

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: 0.5397 Accuracy: 0.8356

TipoOtherModelo baseMCG-NJU/videomae-base-finetuned-kineticsVersãoRevision 1f724f8Bnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
329 MB
Modelo base
MCG-NJU/videomae-base-finetuned-kinetics
Versão
vRevision 1f724f8
Atualizado
10 de out. de 2026
Downloads
7
Fonte
Hugging Face

Descrição da fonte

videomae-base-finetuned-kinetics-finetuned

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: 0.5397
  • Accuracy: 0.8356

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 OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 0.1
  • training_steps: 240

Training results

Training LossEpochStepValidation LossAccuracy
0.58260.2542610.76500.7807
0.35851.25421220.63350.7719
0.19132.25421830.68320.7895
0.09913.23752400.74840.7632

Framework versions

  • Transformers 5.12.1
  • Pytorch 2.12.1+cu130
  • Datasets 5.0.0
  • Tokenizers 0.22.2

Fontes

1 fonte
Hugging FacePrimárioVersão 1f724f83387d27997b9120d8abe1202c3d01aeb9
huggingface.co/thenextme/videomae-base-finetuned-kinetics-finetuned/tree/1f724f83387d27997b9120d8abe1202c3d01aeb9

Verificado 10 de out.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 329 MB · SHA-256 52adfe37e189…b7b6 · Hugging Face

Baixar

Trechos de fonte

3 trechos

--- 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-finetuned 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-finetuned 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: 0.5397 - Accuracy: 0.8356 ## 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 OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.1 - training_steps: 240 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.5826 | 0.2542 | 61 | 0.7650 | 0.7807 | | 0.3585 | 1.2542 | 122 | 0.6335 | 0.7719 | | 0.1913 | 2.2542 | 183 | 0.6832 | 0.7895 | | 0.0991 | 3.2375 | 240 | 0.7484 | 0.7632 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.12.1+cu130 - Datasets 5.0.0 - Tokenizers 0.22.2

Source context: 7 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo thenextme/videomae-base-finetuned-kinetics-finetuned

Source context: 60 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo thenextme/videomae-base-finetuned-kinetics-finetuned