Downloads · 30 days
3
14% of all-time downloads
videomae-finetuned
kharato/videomae-finetuned
videomae-finetuned is a machine learning model from kharato. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
21
Public
Parameters
86.2M
1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors345 MB · 100%
At a glance
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- Aug 7, 2024
- Updated
- Aug 7, 2024
- SHA
- a11eab6e
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-finetuned
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: 0.1289
- Accuracy: 0.9706
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: 0.2
- training_steps: 8620
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7657 | 0.05 | 432 | 0.6104 | 0.8243 |
| 0.4011 | 1.05 | 864 | 0.4283 | 0.8896 |
| 0.301 | 2.05 | 1296 | 0.3957 | 0.8833 |
| 0.2967 | 3.05 | 1728 | 0.3066 | 0.9138 |
| 0.229 | 4.05 | 2160 | 0.3268 | 0.9092 |
| 0.2185 | 5.05 | 2592 | 0.2865 | 0.9281 |
| 0.1863 | 6.05 | 3024 | 0.2550 | 0.9320 |
| 0.1386 | 7.05 | 3456 | 0.2366 | 0.9422 |
| 0.1542 | 8.05 | 3888 | 0.2162 | 0.9449 |
| 0.1396 | 9.05 | 4320 | 0.2069 | 0.9424 |
| 0.1323 | 10.05 | 4752 | 0.2021 | 0.9486 |
| 0.1008 | 11.05 | 5184 | 0.2408 | 0.9465 |
| 0.0756 | 12.05 | 5616 | 0.2023 | 0.9509 |
| 0.0628 | 13.05 | 6048 | 0.1730 | 0.9609 |
| 0.0747 | 14.05 | 6480 | 0.1747 | 0.9622 |
| 0.047 | 15.05 | 6912 | 0.1698 | 0.9629 |
| 0.0586 | 16.05 | 7344 | 0.1412 | 0.9668 |
| 0.0644 | 17.05 | 7776 | 0.1314 | 0.9704 |
| 0.0521 | 18.05 | 8208 | 0.1343 | 0.9688 |
| 0.0516 | 19.05 | 8620 | 0.1289 | 0.9706 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Aug 7, 2024
- Updated
- Aug 7, 2024