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ALL_RGBCROP_ori16F-8B16F-GACWDlrfp16
TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlrfp16
ALL_RGBCROP_ori16F-8B16F-GACWDlrfp16 is a video classification model from TanAlexanderlz. Use it for the video classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Video Classification
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- May 26, 2025
- Updated
- May 26, 2025
- SHA
- e6c9a2a9
Base models
Model card
From the Hugging Face model README
<!-- 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-GACWDlrfp16
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: 0.3899
- Accuracy: 0.8084
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 384
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6923 | 0.0625 | 24 | 0.6734 | 0.6037 |
| 0.6078 | 1.0625 | 48 | 0.6133 | 0.6829 |
| 0.4025 | 2.0625 | 72 | 0.5369 | 0.75 |
| 0.2942 | 3.0625 | 96 | 0.4767 | 0.7927 |
| 0.171 | 4.0625 | 120 | 0.4655 | 0.7927 |
| 0.1159 | 5.0625 | 144 | 0.4691 | 0.8110 |
| 0.0592 | 6.0625 | 168 | 0.5043 | 0.7927 |
| 0.0371 | 7.0625 | 192 | 0.5547 | 0.7927 |
| 0.0205 | 8.0625 | 216 | 0.5886 | 0.7988 |
| 0.013 | 9.0625 | 240 | 0.6280 | 0.7988 |
| 0.0062 | 10.0625 | 264 | 0.6809 | 0.7927 |
| 0.0052 | 11.0625 | 288 | 0.6715 | 0.7866 |
| 0.0041 | 12.0625 | 312 | 0.6975 | 0.8049 |
| 0.0038 | 13.0625 | 336 | 0.7200 | 0.7988 |
| 0.003 | 14.0625 | 360 | 0.7230 | 0.8049 |
| 0.0032 | 15.0625 | 384 | 0.7239 | 0.8049 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- May 26, 2025
- Updated
- May 26, 2025