Downloads · 30 days
5
2% of all-time downloads
videomae-base-finetuned-ucf101-subset
techtank/videomae-base-finetuned-ucf101-subset
videomae-base-finetuned-ucf101-subset is a video classification model from techtank. 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
290
Public
Repo size
3.5 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin345 MB · 100%
At a glance
- Task
- Video Classification
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- Sep 13, 2023
- Updated
- Sep 13, 2023
- SHA
- 6e8d43d4
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-subset
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: 0.1199
- Accuracy: 0.9714
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 600
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2409 | 0.06 | 37 | 2.2371 | 0.1429 |
| 1.3169 | 1.06 | 75 | 1.1241 | 0.6571 |
| 0.5831 | 2.06 | 112 | 0.5958 | 0.7857 |
| 0.5517 | 3.06 | 150 | 0.4112 | 0.8143 |
| 0.398 | 4.06 | 187 | 0.3376 | 0.8429 |
| 0.1959 | 5.06 | 225 | 0.4228 | 0.8857 |
| 0.1159 | 6.06 | 262 | 0.3382 | 0.8571 |
| 0.015 | 7.06 | 300 | 0.3205 | 0.9 |
| 0.0316 | 8.06 | 337 | 0.3495 | 0.8857 |
| 0.0242 | 9.06 | 375 | 0.1675 | 0.9429 |
| 0.005 | 10.06 | 412 | 0.2990 | 0.9286 |
| 0.0047 | 11.06 | 450 | 0.1553 | 0.9429 |
| 0.0044 | 12.06 | 487 | 0.1390 | 0.9571 |
| 0.0039 | 13.06 | 525 | 0.1406 | 0.9429 |
| 0.0107 | 14.06 | 562 | 0.1184 | 0.9571 |
| 0.0034 | 15.06 | 600 | 0.1199 | 0.9714 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Sep 13, 2023
- Updated
- Sep 13, 2023