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ALL RGBCROP Ori16F 8B16F GACWDlr5e 6

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: 0.4757 Accuracy: 0.8443

Sourcehuggingface.co/TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6/tree/49b902c5ba3167a5d03e3bfb505e742805576cf7
KindOtherBase modelMCG-NJU/videomae-base-finetuned-kineticsVersionRevision 49b902c

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base-finetuned-kinetics
Version
vRevision 49b902c
Updated
Sep 26, 2026
Downloads
1
Source
Hugging Face

Source description

ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6

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: 0.4757
  • Accuracy: 0.8443

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-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.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: 0.1
  • training_steps: 768

Training results

Training LossEpochStepValidation LossAccuracy
0.69520.0625480.70220.5244
0.59821.0625960.64750.6463
0.43292.06251440.59110.7134
0.37523.06251920.53380.7378
0.22024.06252400.51090.75
0.18935.06252880.49910.7439
0.14546.06253360.53130.7439
0.09317.06253840.56680.7439
0.06578.06254320.60280.7622
0.07049.06254800.63400.7744
0.029810.06255280.67700.7683
0.03111.06255760.70890.7805
0.026512.06256240.74150.7683
0.023813.06256720.77030.7744
0.014314.06257200.78570.7744
0.008215.06257680.79220.7683

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1

Sources

1 source
Hugging FacePrimaryVersion 49b902c5ba3167a5d03e3bfb505e742805576cf7
huggingface.co/TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6/tree/49b902c5ba3167a5d03e3bfb505e742805576cf7

Verified Sep 12

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 84a6a1c11992…c314 · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 12huggingface.co/TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6

--- 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: ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6 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. --> # ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6 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: 0.4757 - Accuracy: 0.8443 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.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: 0.1 - training_steps: 768 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.6952 | 0.0625 | 48 | 0.7022 | 0.5244 | | 0.5982 | 1.0625 | 96 | 0.6475 | 0.6463 | | 0.4329 | 2.0625 | 144 | 0.5911 | 0.7134 | | 0.3752 | 3.0625 | 192 | 0.5338 | 0.7378 | | 0.2202 | 4.0625 | 240 | 0.5109 | 0.75 | | 0.1893 | 5.0625 | 288 | 0.4991 | 0.7439 | | 0.1454 | 6.0625 | 336 | 0.5313 | 0.7439 | | 0.0931 | 7.0625 | 384 | 0.5668 | 0.7439 | | 0.0657 | 8.0625 | 432 | 0.6028 | 0.7622 | | 0.0704 | 9.0625 | 480 | 0.6340 | 0.7744 | | 0.0298 | 10.0625 | 528 | 0.6770 | 0.7683 | | 0.031 | 11.0625 | 576 | 0.7089 | 0.7805 | | 0.0265 | 12.0625 | 624 | 0.7415 | 0.7683 | | 0.0238 | 13.0625 | 672 | 0.7703 | 0.7744 | | 0.0143 | 14.0625 | 720 | 0.7857 | 0.7744 | | 0.0082 | 15.0625 | 768 | 0.7922 | 0.7683 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

NoteSep 12huggingface.co/TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6

Source context: 1 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlr5e-6