Instructions to use mosesb/drowsiness-detection-mobileViT-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mosesb/drowsiness-detection-mobileViT-v2 with timm:
import timm model = timm.create_model("hf-hub:mosesb/drowsiness-detection-mobileViT-v2", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download training_history.csv from mosesb/drowsiness-detection-mobileViT-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.52 kB
-
https://huggingface.co/mosesb/drowsiness-detection-mobileViT-v2/resolve/main/training_history.csv
- Command line
-
hf download hf://mosesb/drowsiness-detection-mobileViT-v2/training_history.csv
-
curl -L -o training_history.csv https://huggingface.co/mosesb/drowsiness-detection-mobileViT-v2/resolve/main/training_history.csv
2.52 kB
| epoch,train_loss,train_acc,val_loss,val_acc | |
| 1,0.005245581285077896,0.9985433537428403,0.0022536597098549594,0.9993005036373812 | |
| 2,0.005058018809589851,0.9984445980643888,0.0023724412707676803,0.9993005036373812 | |
| 3,0.00333882693223768,0.9990618210547108,0.004640081306103603,0.9981813094571909 | |
| 4,0.0019675480249330287,0.9994321548489039,0.0011330571216904648,0.9995803021824288 | |
| 5,0.0009190954186649758,0.9996790440450326,0.0042279156476276464,0.9987409065472861 | |
| 6,0.003519932303200358,0.9989136875370335,0.0022254574140079496,0.999440402909905 | |
| 7,0.0008493974372590355,0.9998024886430971,0.005408237440245763,0.9987409065472861 | |
| 8,0.0012583149986798944,0.9995309105273553,0.0014416664605325462,0.9995803021824288 | |
| 9,0.0004477896281065585,0.9998765554019357,0.0015333133928007877,0.9995803021824288 | |
| 10,0.0010184175027428194,0.9996790440450326,0.0008335669395869618,0.9998601007274763 | |
| 11,0.0004673596799982551,0.9998518664823228,0.00048003266577130574,0.9998601007274763 | |
| 12,0.0004278480958328559,0.9998765554019357,0.0010320477756580264,0.9997202014549526 | |
| 13,0.0006154210043430926,0.9998518664823228,0.001365777820691367,0.999440402909905 | |
| 14,0.00031554297358610365,0.9999012443215486,0.0020125484583530568,0.9995803021824288 | |
| 15,0.0008148343436515399,0.9998024886430971,0.0009892107681903222,0.9998601007274763 | |
| 16,0.00044639887271710017,0.9998518664823228,0.0007288139932215199,0.9995803021824288 | |
| 17,0.0001811253026875362,0.9999753110803872,0.0005784849157645884,0.9995803021824288 | |
| 18,0.00046878313578802293,0.9999259332411614,0.0007865349200535725,0.9997202014549526 | |
| 19,6.448918337161184e-05,1.0,0.0007113339355956221,0.999440402909905 | |
| 20,0.00033571305326825105,0.9999259332411614,0.0013030710574786868,0.9995803021824288 | |
| 21,4.827969234206115e-05,0.9999753110803872,0.000493603309694494,0.9995803021824288 | |
| 22,2.9587593322939357e-05,1.0,0.0005621903976394485,0.9998601007274763 | |
| 23,0.0002729453408775668,0.9999259332411614,0.0005450556711127411,0.9997202014549526 | |
| 24,5.782559405570643e-05,0.9999753110803872,0.0006117059190368832,0.9997202014549526 | |
| 25,9.650301194302824e-05,0.9999753110803872,0.0015031366452237724,0.9995803021824288 | |
| 26,0.00018091677156248143,0.9999753110803872,0.000420644104269485,0.9998601007274763 | |
| 27,0.00040603304785788484,0.9999259332411614,0.0009131295740309233,0.9995803021824288 | |
| 28,1.6794279317459968e-05,1.0,0.0007172291170396112,0.9995803021824288 | |
| 29,4.037580577003857e-05,1.0,0.0006496535298990078,0.9995803021824288 | |
| 30,3.2526824202515245e-05,0.9999753110803872,0.0006385279186205687,0.9995803021824288 | |