Image Segmentation
Transformers
Safetensors
mask2former
instance-segmentation
vision
Generated from Trainer
Instructions to use slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former") model = Mask2FormerForUniversalSegmentation.from_pretrained("slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former") - Notebooks
- Google Colab
- Kaggle
finetune-instance-segmentation-alpha-dent-mask2former
This model is a fine-tuned version of slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former on the slnkvdns/AlphaDent dataset. It achieves the following results on the evaluation set:
- Loss: 26.6500
- Map: 0.2861
- Map 50: 0.425
- Map 75: 0.2873
- Map Small: 0.1204
- Map Medium: 0.3288
- Map Large: 0.9391
- Mar 1: 0.1955
- Mar 10: 0.3752
- Mar 100: 0.3935
- Mar Small: 0.2283
- Mar Medium: 0.4298
- Mar Large: 0.9416
- Map Background: 0.9668
- Mar 100 Background: 0.9747
- Map Abrasion: 0.659
- Mar 100 Abrasion: 0.8286
- Map Filling: 0.2331
- Mar 100 Filling: 0.3693
- Map Crown: 0.6927
- Mar 100 Crown: 0.7263
- Map Caries class 1: 0.1201
- Mar 100 Caries class 1: 0.2672
- Map Caries class 2: 0.0486
- Mar 100 Caries class 2: 0.2292
- Map Caries class 3: 0.005
- Mar 100 Caries class 3: 0.0758
- Map Caries class 4: 0.0028
- Mar 100 Caries class 4: 0.1
- Map Caries class 5: 0.1192
- Mar 100 Caries class 5: 0.2436
- Map Caries class 6: 0.0135
- Mar 100 Caries class 6: 0.12
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Background | Mar 100 Background | Map Abrasion | Mar 100 Abrasion | Map Filling | Mar 100 Filling | Map Crown | Mar 100 Crown | Map Caries class 1 | Mar 100 Caries class 1 | Map Caries class 2 | Mar 100 Caries class 2 | Map Caries class 3 | Mar 100 Caries class 3 | Map Caries class 4 | Mar 100 Caries class 4 | Map Caries class 5 | Mar 100 Caries class 5 | Map Caries class 6 | Mar 100 Caries class 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17.9799 | 1.0 | 155 | 24.1491 | 0.2825 | 0.4176 | 0.2842 | 0.1173 | 0.3169 | 0.8899 | 0.1842 | 0.3561 | 0.3783 | 0.2077 | 0.4217 | 0.9416 | 0.9678 | 0.9747 | 0.6737 | 0.8454 | 0.2432 | 0.395 | 0.6666 | 0.7263 | 0.1127 | 0.2621 | 0.0312 | 0.1764 | 0.0071 | 0.1 | 0.0011 | 0.025 | 0.1187 | 0.2577 | 0.0035 | 0.02 |
| 17.2483 | 2.0 | 310 | 24.3290 | 0.2887 | 0.429 | 0.2872 | 0.1255 | 0.3259 | 0.9385 | 0.2001 | 0.3738 | 0.39 | 0.2269 | 0.4209 | 0.9408 | 0.965 | 0.9723 | 0.67 | 0.8471 | 0.2452 | 0.3765 | 0.6667 | 0.7211 | 0.1229 | 0.231 | 0.0316 | 0.1875 | 0.0055 | 0.0939 | 0.0115 | 0.125 | 0.1266 | 0.2654 | 0.0416 | 0.08 |
| 16.9506 | 3.0 | 465 | 24.6849 | 0.2829 | 0.4108 | 0.2832 | 0.1231 | 0.3163 | 0.9391 | 0.1777 | 0.3604 | 0.3784 | 0.2088 | 0.4175 | 0.9416 | 0.9669 | 0.9747 | 0.6682 | 0.8426 | 0.2406 | 0.381 | 0.6506 | 0.7263 | 0.1319 | 0.2483 | 0.0324 | 0.1972 | 0.0085 | 0.0879 | 0.0 | 0.0 | 0.1149 | 0.2462 | 0.0156 | 0.08 |
| 16.7543 | 4.0 | 620 | 25.2413 | 0.2871 | 0.4272 | 0.2895 | 0.1227 | 0.3107 | 0.8882 | 0.183 | 0.3697 | 0.3868 | 0.2175 | 0.4118 | 0.9408 | 0.9626 | 0.9723 | 0.6622 | 0.8348 | 0.2343 | 0.386 | 0.6988 | 0.7632 | 0.118 | 0.2534 | 0.0435 | 0.1986 | 0.0054 | 0.0939 | 0.0 | 0.0 | 0.121 | 0.2462 | 0.0254 | 0.12 |
| 16.3350 | 5.0 | 775 | 25.6590 | 0.2832 | 0.4136 | 0.2814 | 0.1205 | 0.3136 | 0.9403 | 0.1807 | 0.3585 | 0.3736 | 0.2062 | 0.3962 | 0.9428 | 0.9703 | 0.9783 | 0.6595 | 0.8328 | 0.2347 | 0.3654 | 0.6683 | 0.7211 | 0.1216 | 0.2466 | 0.03 | 0.1736 | 0.0049 | 0.0909 | 0.0 | 0.0 | 0.1214 | 0.2474 | 0.0208 | 0.08 |
| 16.0730 | 6.0 | 930 | 25.5466 | 0.2887 | 0.4254 | 0.2901 | 0.1239 | 0.3183 | 0.9385 | 0.1857 | 0.37 | 0.3867 | 0.2171 | 0.4078 | 0.9416 | 0.9651 | 0.9747 | 0.6593 | 0.8308 | 0.2417 | 0.3832 | 0.7016 | 0.7684 | 0.1266 | 0.2638 | 0.0402 | 0.1833 | 0.0048 | 0.097 | 0.0006 | 0.025 | 0.126 | 0.241 | 0.0217 | 0.1 |
| 15.8823 | 7.0 | 1085 | 25.9349 | 0.2877 | 0.4215 | 0.2863 | 0.122 | 0.3234 | 0.9401 | 0.1823 | 0.3764 | 0.3956 | 0.2255 | 0.4378 | 0.9424 | 0.9698 | 0.9771 | 0.6594 | 0.8298 | 0.2411 | 0.3732 | 0.7049 | 0.7737 | 0.1227 | 0.2707 | 0.0414 | 0.2028 | 0.0051 | 0.0848 | 0.0008 | 0.05 | 0.1218 | 0.2538 | 0.01 | 0.14 |
| 15.5856 | 8.0 | 1240 | 25.6096 | 0.2891 | 0.4289 | 0.2905 | 0.1226 | 0.3211 | 0.9391 | 0.1913 | 0.379 | 0.3951 | 0.2312 | 0.4162 | 0.942 | 0.9667 | 0.9759 | 0.6605 | 0.8228 | 0.2371 | 0.3737 | 0.7092 | 0.7579 | 0.1303 | 0.2707 | 0.0372 | 0.2028 | 0.0049 | 0.1061 | 0.0021 | 0.1 | 0.1302 | 0.241 | 0.0129 | 0.1 |
| 15.2916 | 9.0 | 1395 | 25.5277 | 0.2904 | 0.4282 | 0.2877 | 0.1228 | 0.3212 | 0.9388 | 0.1935 | 0.3802 | 0.3982 | 0.2291 | 0.4252 | 0.9412 | 0.966 | 0.9735 | 0.6601 | 0.8248 | 0.2396 | 0.3883 | 0.7266 | 0.7737 | 0.1227 | 0.2759 | 0.0365 | 0.1931 | 0.0067 | 0.0939 | 0.0023 | 0.075 | 0.1333 | 0.2436 | 0.0102 | 0.14 |
| 15.1850 | 10.0 | 1550 | 26.6500 | 0.2861 | 0.425 | 0.2873 | 0.1204 | 0.3288 | 0.9391 | 0.1955 | 0.3752 | 0.3935 | 0.2283 | 0.4298 | 0.9416 | 0.9668 | 0.9747 | 0.659 | 0.8286 | 0.2331 | 0.3693 | 0.6927 | 0.7263 | 0.1201 | 0.2672 | 0.0486 | 0.2292 | 0.005 | 0.0758 | 0.0028 | 0.1 | 0.1192 | 0.2436 | 0.0135 | 0.12 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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