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videomae-base-finetuned-ucf101-relevancedetection-surgical
Shangong/videomae-base-finetuned-ucf101-relevancedetection-surgical
videomae-base-finetuned-ucf101-relevancedetection-surgical is a video classification model from Shangong. 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
- Aug 27, 2024
- Updated
- Sep 3, 2024
- SHA
- 2856063e
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. -->
videomae-base-finetuned-ucf101-relevancedetection-surgical
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: 0.0913
- Accuracy: 0.9828
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 7370
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2187 | 0.0501 | 369 | 2.3587 | 0.5141 |
| 0.2004 | 1.0501 | 738 | 0.3347 | 0.8992 |
| 0.0702 | 2.0501 | 1107 | 0.1048 | 0.9657 |
| 0.0174 | 3.0501 | 1476 | 0.1231 | 0.9637 |
| 0.0463 | 4.0501 | 1845 | 0.5684 | 0.9012 |
| 0.0008 | 5.0501 | 2214 | 1.7179 | 0.7702 |
| 0.022 | 6.0501 | 2583 | 0.4177 | 0.8972 |
| 0.0055 | 7.0501 | 2952 | 0.0563 | 0.9738 |
| 0.0303 | 8.0501 | 3321 | 0.2138 | 0.9657 |
| 0.001 | 9.0501 | 3690 | 0.4224 | 0.9133 |
| 0.0004 | 10.0501 | 4059 | 0.2029 | 0.9577 |
| 0.0003 | 11.0501 | 4428 | 0.4289 | 0.9375 |
| 0.0002 | 12.0501 | 4797 | 0.5915 | 0.9214 |
| 0.0001 | 13.0501 | 5166 | 0.1033 | 0.9718 |
| 0.0001 | 14.0501 | 5535 | 0.1252 | 0.9677 |
| 0.0002 | 15.0501 | 5904 | 0.1371 | 0.9617 |
| 0.0001 | 16.0501 | 6273 | 0.0273 | 0.9919 |
| 0.0002 | 17.0501 | 6642 | 0.0450 | 0.9899 |
| 0.0001 | 18.0501 | 7011 | 0.0284 | 0.9899 |
| 0.0001 | 19.0487 | 7370 | 0.0224 | 0.9940 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Aug 27, 2024
- Updated
- Sep 3, 2024