Downloads · 30 days
5
19% of all-time downloads
VideoMAE_default_fold__3__10_epoch_bdslw60_kinetics_25_2_25
Shawon16/VideoMAE_default_fold__3__10_epoch_bdslw60_kinetics_25_2_25
VideoMAE_default_fold__3__10_epoch_bdslw60_kinetics_25_2_25 is a video classification model from Shawon16. 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. --
All-time downloads
26
Public
Parameters
86.3M
346 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors345 MB · 100%
At a glance
- Task
- Video Classification
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- Feb 26, 2025
- Updated
- Feb 26, 2025
- SHA
- bcfb8ddb
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_default_fold__3__10_epoch_bdslw60_kinetics_25_2_25
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: 0.2273
- Accuracy: 0.9506
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use 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: 9030
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.387 | 0.1 | 903 | 0.2921 | 0.9552 |
| 0.183 | 1.1001 | 1807 | 0.0394 | 0.9888 |
| 0.0299 | 2.1 | 2710 | 0.0423 | 0.9900 |
| 0.0776 | 3.1001 | 3614 | 0.0230 | 0.9913 |
| 0.0583 | 4.1 | 4517 | 0.0582 | 0.9875 |
| 0.0162 | 5.1001 | 5421 | 0.0255 | 0.9963 |
| 0.0019 | 6.1 | 6324 | 0.0167 | 0.9975 |
| 0.0011 | 7.1001 | 7228 | 0.0164 | 0.9975 |
| 0.0002 | 8.1 | 8131 | 0.0141 | 0.9975 |
| 0.0066 | 9.0995 | 9030 | 0.0151 | 0.9988 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Feb 26, 2025
- Updated
- Feb 26, 2025