|
--- |
|
license: apache-2.0 |
|
base_model: google/vit-base-patch16-224-in21k |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: Action_model |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: imagefolder |
|
type: imagefolder |
|
config: default |
|
split: train |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.7619047619047619 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# Action_model |
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2147 |
|
- Accuracy: 0.7619 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.1624 | 0.16 | 100 | 1.0534 | 0.7638 | |
|
| 0.2926 | 0.32 | 200 | 1.3484 | 0.6867 | |
|
| 0.159 | 0.48 | 300 | 0.9484 | 0.7724 | |
|
| 0.2145 | 0.64 | 400 | 1.0014 | 0.7476 | |
|
| 0.1889 | 0.8 | 500 | 1.0321 | 0.7457 | |
|
| 0.3064 | 0.96 | 600 | 1.0795 | 0.7314 | |
|
| 0.2195 | 1.11 | 700 | 0.9886 | 0.7629 | |
|
| 0.2982 | 1.27 | 800 | 1.0292 | 0.7590 | |
|
| 0.2477 | 1.43 | 900 | 1.2391 | 0.7248 | |
|
| 0.3076 | 1.59 | 1000 | 1.1326 | 0.7324 | |
|
| 0.1863 | 1.75 | 1100 | 1.2596 | 0.7048 | |
|
| 0.2577 | 1.91 | 1200 | 1.0649 | 0.7610 | |
|
| 0.1491 | 2.07 | 1300 | 1.1044 | 0.7562 | |
|
| 0.2635 | 2.23 | 1400 | 1.1965 | 0.7448 | |
|
| 0.2597 | 2.39 | 1500 | 1.2241 | 0.7429 | |
|
| 0.2468 | 2.55 | 1600 | 1.1452 | 0.7390 | |
|
| 0.216 | 2.71 | 1700 | 1.2419 | 0.7276 | |
|
| 0.1971 | 2.87 | 1800 | 1.1883 | 0.7362 | |
|
| 0.2071 | 3.03 | 1900 | 1.4659 | 0.6952 | |
|
| 0.1535 | 3.18 | 2000 | 1.0239 | 0.7724 | |
|
| 0.1842 | 3.34 | 2100 | 1.1967 | 0.7390 | |
|
| 0.2087 | 3.5 | 2200 | 1.1403 | 0.7467 | |
|
| 0.1658 | 3.66 | 2300 | 1.2901 | 0.7343 | |
|
| 0.1159 | 3.82 | 2400 | 1.1826 | 0.7438 | |
|
| 0.1498 | 3.98 | 2500 | 1.2627 | 0.7419 | |
|
| 0.135 | 4.14 | 2600 | 1.1383 | 0.76 | |
|
| 0.1492 | 4.3 | 2700 | 1.2310 | 0.7343 | |
|
| 0.0982 | 4.46 | 2800 | 1.4144 | 0.7105 | |
|
| 0.1256 | 4.62 | 2900 | 1.3513 | 0.7171 | |
|
| 0.1544 | 4.78 | 3000 | 1.4280 | 0.7019 | |
|
| 0.0858 | 4.94 | 3100 | 1.2231 | 0.7429 | |
|
| 0.1049 | 5.1 | 3200 | 1.2775 | 0.7352 | |
|
| 0.1361 | 5.25 | 3300 | 1.2840 | 0.7429 | |
|
| 0.1505 | 5.41 | 3400 | 1.3373 | 0.7390 | |
|
| 0.1244 | 5.57 | 3500 | 1.2959 | 0.7438 | |
|
| 0.1114 | 5.73 | 3600 | 1.3181 | 0.7381 | |
|
| 0.0851 | 5.89 | 3700 | 1.3288 | 0.7457 | |
|
| 0.0799 | 6.05 | 3800 | 1.1859 | 0.76 | |
|
| 0.1331 | 6.21 | 3900 | 1.2544 | 0.7371 | |
|
| 0.121 | 6.37 | 4000 | 1.2186 | 0.7533 | |
|
| 0.1276 | 6.53 | 4100 | 1.2964 | 0.7324 | |
|
| 0.1194 | 6.69 | 4200 | 1.1907 | 0.7590 | |
|
| 0.1649 | 6.85 | 4300 | 1.4679 | 0.7105 | |
|
| 0.0558 | 7.01 | 4400 | 1.2028 | 0.7533 | |
|
| 0.0687 | 7.17 | 4500 | 1.3242 | 0.7381 | |
|
| 0.1419 | 7.32 | 4600 | 1.2328 | 0.76 | |
|
| 0.0901 | 7.48 | 4700 | 1.1861 | 0.7676 | |
|
| 0.1181 | 7.64 | 4800 | 1.4031 | 0.7352 | |
|
| 0.1272 | 7.8 | 4900 | 1.3608 | 0.7438 | |
|
| 0.0979 | 7.96 | 5000 | 1.3098 | 0.7495 | |
|
| 0.0805 | 8.12 | 5100 | 1.2445 | 0.7533 | |
|
| 0.0354 | 8.28 | 5200 | 1.2345 | 0.7581 | |
|
| 0.0499 | 8.44 | 5300 | 1.1776 | 0.7571 | |
|
| 0.1046 | 8.6 | 5400 | 1.1939 | 0.76 | |
|
| 0.0912 | 8.76 | 5500 | 1.2373 | 0.7486 | |
|
| 0.0589 | 8.92 | 5600 | 1.2165 | 0.7552 | |
|
| 0.0829 | 9.08 | 5700 | 1.2684 | 0.7505 | |
|
| 0.0897 | 9.24 | 5800 | 1.2467 | 0.7552 | |
|
| 0.1114 | 9.39 | 5900 | 1.2303 | 0.7571 | |
|
| 0.0712 | 9.55 | 6000 | 1.1997 | 0.7638 | |
|
| 0.0621 | 9.71 | 6100 | 1.2094 | 0.7629 | |
|
| 0.037 | 9.87 | 6200 | 1.2147 | 0.7619 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
