update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
metrics:
|
| 6 |
+
- accuracy
|
| 7 |
+
model-index:
|
| 8 |
+
- name: videomae-base-finetuned-ASBD_Augm
|
| 9 |
+
results: []
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 13 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 14 |
+
|
| 15 |
+
# videomae-base-finetuned-ASBD_Augm
|
| 16 |
+
|
| 17 |
+
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
|
| 18 |
+
It achieves the following results on the evaluation set:
|
| 19 |
+
- Loss: 1.0073
|
| 20 |
+
- Accuracy: 0.7647
|
| 21 |
+
|
| 22 |
+
## Model description
|
| 23 |
+
|
| 24 |
+
More information needed
|
| 25 |
+
|
| 26 |
+
## Intended uses & limitations
|
| 27 |
+
|
| 28 |
+
More information needed
|
| 29 |
+
|
| 30 |
+
## Training and evaluation data
|
| 31 |
+
|
| 32 |
+
More information needed
|
| 33 |
+
|
| 34 |
+
## Training procedure
|
| 35 |
+
|
| 36 |
+
### Training hyperparameters
|
| 37 |
+
|
| 38 |
+
The following hyperparameters were used during training:
|
| 39 |
+
- learning_rate: 5e-05
|
| 40 |
+
- train_batch_size: 12
|
| 41 |
+
- eval_batch_size: 12
|
| 42 |
+
- seed: 42
|
| 43 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 44 |
+
- lr_scheduler_type: linear
|
| 45 |
+
- lr_scheduler_warmup_ratio: 0.1
|
| 46 |
+
- training_steps: 1050
|
| 47 |
+
|
| 48 |
+
### Training results
|
| 49 |
+
|
| 50 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
| 51 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
| 52 |
+
| 1.0908 | 0.09 | 97 | 1.6048 | 0.2838 |
|
| 53 |
+
| 1.0824 | 1.09 | 194 | 1.0615 | 0.4459 |
|
| 54 |
+
| 0.4003 | 2.09 | 291 | 1.4701 | 0.6081 |
|
| 55 |
+
| 0.4568 | 3.09 | 388 | 2.2165 | 0.4595 |
|
| 56 |
+
| 0.133 | 4.09 | 485 | 2.0913 | 0.5135 |
|
| 57 |
+
| 0.07 | 5.09 | 582 | 1.7235 | 0.6081 |
|
| 58 |
+
| 0.018 | 6.09 | 679 | 1.8975 | 0.6622 |
|
| 59 |
+
| 0.0162 | 7.09 | 776 | 2.0286 | 0.6622 |
|
| 60 |
+
| 0.0019 | 8.09 | 873 | 2.0696 | 0.6486 |
|
| 61 |
+
| 0.0012 | 9.09 | 970 | 1.8754 | 0.6892 |
|
| 62 |
+
| 0.0042 | 10.08 | 1050 | 1.8898 | 0.6757 |
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
### Framework versions
|
| 66 |
+
|
| 67 |
+
- Transformers 4.28.0
|
| 68 |
+
- Pytorch 2.1.0+cu118
|
| 69 |
+
- Datasets 2.14.5
|
| 70 |
+
- Tokenizers 0.13.3
|