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Shot Model

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: 1.4299 Accuracy: 0.692

Fuentehuggingface.co/ashishgimekar/shot_model/tree/7644439362531d1bb521b9af00454a5cbadac65b
TipoOtherModelo baseMCG-NJU/videomae-baseVersiónRevision 7644439Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
329 MB
Modelo base
MCG-NJU/videomae-base
Versión
vRevision 7644439
Actualizado
24 oct 2026
Descargas
4
Fuente
Hugging Face

Descripción de la fuente

shot_model

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: 1.4299
  • Accuracy: 0.692

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
  • 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: 6250

Training results

Training LossEpochStepValidation LossAccuracy
1.62271.06251.92350.228
1.42692.012501.44250.496
1.02223.018751.33760.5
1.06164.025001.61640.464
1.05475.031251.22870.552
0.60926.037501.39960.584
0.52177.043751.28990.644
0.77618.050001.50180.656
1.20099.056251.48670.676
0.230410.062501.42990.692

Framework versions

  • Transformers 5.3.0
  • Pytorch 2.10.0
  • Datasets 4.6.1
  • Tokenizers 0.22.2

Fuentes

1 fuente
Hugging FacePrincipalVersión 7644439362531d1bb521b9af00454a5cbadac65b
huggingface.co/ashishgimekar/shot_model/tree/7644439362531d1bb521b9af00454a5cbadac65b

Verificado 24 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 329 MB · SHA-256 5a78ae957b94…8939 · Hugging Face

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Extractos de fuentes

3 extractos
Readme6 agohuggingface.co/ashishgimekar/shot_model

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: shot_model 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. --> # shot_model 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: 1.4299 - Accuracy: 0.692 ## 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 - 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: 6250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6227 | 1.0 | 625 | 1.9235 | 0.228 | | 1.4269 | 2.0 | 1250 | 1.4425 | 0.496 | | 1.0222 | 3.0 | 1875 | 1.3376 | 0.5 | | 1.0616 | 4.0 | 2500 | 1.6164 | 0.464 | | 1.0547 | 5.0 | 3125 | 1.2287 | 0.552 | | 0.6092 | 6.0 | 3750 | 1.3996 | 0.584 | | 0.5217 | 7.0 | 4375 | 1.2899 | 0.644 | | 0.7761 | 8.0 | 5000 | 1.5018 | 0.656 | | 1.2009 | 9.0 | 5625 | 1.4867 | 0.676 | | 0.2304 | 10.0 | 6250 | 1.4299 | 0.692 | ### Framework versions - Transformers 5.3.0 - Pytorch 2.10.0 - Datasets 4.6.1 - Tokenizers 0.22.2

Note24 octhuggingface.co/ashishgimekar/shot_model

Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo ashishgimekar/shot_model

Note6 agohuggingface.co/ashishgimekar/shot_model

Source context: 5 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo ashishgimekar/shot_model