Downloads · 30 days
4
19% of all-time downloads
ucf101_videomae-base
jialicheng/ucf101_videomae-base
ucf101_videomae-base is a video classification model from jialicheng. Use it for the video classification 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
86.3M
353 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors345 MB · 98%
At a glance
- Task
- Video Classification
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- Aug 27, 2025
- Updated
- Aug 27, 2025
- SHA
- 8b75f28b
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. -->
ucf101_42
This model is a fine-tuned version of MCG-NJU/videomae-base on the ucf101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8198
- Accuracy: 0.8298
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: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.01 | 300 | 2.3345 | 0.4887 |
| No log | 2.01 | 600 | 1.4956 | 0.6433 |
| No log | 3.01 | 900 | 1.1647 | 0.7162 |
| 1.5784 | 4.01 | 1200 | 1.1531 | 0.7029 |
| 1.5784 | 5.01 | 1500 | 1.0251 | 0.7334 |
| 1.5784 | 6.01 | 1800 | 1.0315 | 0.7333 |
| 0.1821 | 7.01 | 2100 | 0.9787 | 0.7617 |
| 0.1821 | 8.01 | 2400 | 0.8933 | 0.7838 |
| 0.1821 | 9.01 | 2700 | 0.8781 | 0.7917 |
| 0.0651 | 10.01 | 3000 | 0.9051 | 0.7910 |
| 0.0651 | 11.01 | 3300 | 0.9593 | 0.7900 |
| 0.0651 | 12.01 | 3600 | 0.8054 | 0.8187 |
| 0.0651 | 13.01 | 3900 | 0.8679 | 0.8142 |
| 0.0265 | 14.01 | 4200 | 0.8380 | 0.8208 |
| 0.0265 | 15.01 | 4500 | 0.8317 | 0.8247 |
| 0.0265 | 16.01 | 4800 | 0.8027 | 0.8249 |
| 0.0091 | 17.01 | 5100 | 0.8240 | 0.8255 |
| 0.0091 | 18.01 | 5400 | 0.8480 | 0.8211 |
| 0.0091 | 19.01 | 5700 | 0.8198 | 0.8298 |
| 0.0091 | 19.87 | 5960 | 0.8315 | 0.8283 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Video Classification
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Aug 27, 2025
- Updated
- Aug 27, 2025