Image Segmentation
Transformers
Safetensors
mask2former
instance-segmentation
vision
Generated from Trainer
Instructions to use slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former-base 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-base 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-base")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former-base") model = Mask2FormerForUniversalSegmentation.from_pretrained("slnkvdns/finetune-instance-segmentation-alpha-dent-mask2former-base") - Notebooks
- Google Colab
- Kaggle
finetune-instance-segmentation-alpha-dent-mask2former-base
This model is a fine-tuned version of facebook/mask2former-swin-small-coco-instance on the slnkvdns/AlphaDent dataset. It achieves the following results on the evaluation set:
- Loss: 24.9112
- Map: 0.2883
- Map 50: 0.4168
- Map 75: 0.2833
- Map Small: 0.1263
- Map Medium: 0.3228
- Map Large: 0.7868
- Mar 1: 0.1931
- Mar 10: 0.3716
- Mar 100: 0.3891
- Mar Small: 0.2192
- Mar Medium: 0.4104
- Mar Large: 0.89
- Map Background: 0.9602
- Mar 100 Background: 0.9699
- Map Abrasion: 0.7008
- Mar 100 Abrasion: 0.8541
- Map Filling: 0.2203
- Mar 100 Filling: 0.3536
- Map Crown: 0.7002
- Mar 100 Crown: 0.8053
- Map Caries class 1: 0.1182
- Mar 100 Caries class 1: 0.2741
- Map Caries class 2: 0.0324
- Mar 100 Caries class 2: 0.1861
- Map Caries class 3: 0.0067
- Mar 100 Caries class 3: 0.0788
- Map Caries class 4: 0.0224
- Mar 100 Caries class 4: 0.1
- Map Caries class 5: 0.1209
- Mar 100 Caries class 5: 0.2487
- Map Caries class 6: 0.0008
- Mar 100 Caries class 6: 0.02
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: 4e-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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45.8256 | 1.0 | 155 | 36.5384 | 0.1558 | 0.1945 | 0.17 | 0.0821 | 0.0991 | 0.3994 | 0.1104 | 0.202 | 0.2208 | 0.1227 | 0.1912 | 0.6044 | 0.8982 | 0.9133 | 0.5921 | 0.8283 | 0.0279 | 0.2145 | 0.0 | 0.0 | 0.0051 | 0.0414 | 0.0005 | 0.0333 | 0.0009 | 0.0121 | 0.0 | 0.0 | 0.0327 | 0.1654 | 0.0 | 0.0 |
| 33.1372 | 2.0 | 310 | 31.3143 | 0.1952 | 0.265 | 0.2015 | 0.0912 | 0.1876 | 0.7196 | 0.1495 | 0.3013 | 0.3234 | 0.1635 | 0.3368 | 0.9014 | 0.9403 | 0.9542 | 0.6287 | 0.8278 | 0.1051 | 0.276 | 0.1867 | 0.6368 | 0.03 | 0.2328 | 0.0058 | 0.0903 | 0.0012 | 0.0212 | 0.0 | 0.0 | 0.0546 | 0.1949 | 0.0 | 0.0 |
| 29.0343 | 3.0 | 465 | 29.2439 | 0.2207 | 0.3169 | 0.2227 | 0.0998 | 0.2434 | 0.8249 | 0.1498 | 0.3302 | 0.3485 | 0.1768 | 0.4005 | 0.9371 | 0.9496 | 0.9614 | 0.6151 | 0.8253 | 0.132 | 0.2961 | 0.3515 | 0.7526 | 0.0745 | 0.2362 | 0.0107 | 0.1319 | 0.0008 | 0.0455 | 0.0 | 0.0 | 0.0732 | 0.2359 | 0.0 | 0.0 |
| 26.3806 | 4.0 | 620 | 26.8526 | 0.2641 | 0.3736 | 0.2652 | 0.1086 | 0.2931 | 0.7625 | 0.1811 | 0.3354 | 0.3542 | 0.1811 | 0.409 | 0.9392 | 0.9569 | 0.9675 | 0.6843 | 0.8511 | 0.1786 | 0.3089 | 0.6277 | 0.7474 | 0.0791 | 0.2552 | 0.0141 | 0.1306 | 0.0018 | 0.0485 | 0.0 | 0.0 | 0.0987 | 0.2333 | 0.0 | 0.0 |
| 24.5939 | 5.0 | 775 | 26.1020 | 0.2691 | 0.3766 | 0.269 | 0.1092 | 0.3087 | 0.7244 | 0.1783 | 0.3438 | 0.3618 | 0.1841 | 0.4174 | 0.9404 | 0.9605 | 0.9711 | 0.6849 | 0.8469 | 0.1718 | 0.3145 | 0.6582 | 0.7947 | 0.0893 | 0.2448 | 0.0236 | 0.1667 | 0.0013 | 0.0545 | 0.0 | 0.0 | 0.1019 | 0.2244 | 0.0 | 0.0 |
| 22.9142 | 6.0 | 930 | 24.8907 | 0.2786 | 0.3964 | 0.279 | 0.1147 | 0.3172 | 0.8896 | 0.1774 | 0.3462 | 0.3614 | 0.1851 | 0.4225 | 0.9416 | 0.9669 | 0.9747 | 0.7054 | 0.8599 | 0.1808 | 0.3106 | 0.671 | 0.7474 | 0.1146 | 0.2569 | 0.0162 | 0.1611 | 0.0038 | 0.0485 | 0.0 | 0.0 | 0.1271 | 0.2551 | 0.0 | 0.0 |
| 21.8794 | 7.0 | 1085 | 24.7008 | 0.2827 | 0.4036 | 0.2816 | 0.1175 | 0.316 | 0.7695 | 0.1805 | 0.351 | 0.3688 | 0.1947 | 0.4051 | 0.9229 | 0.9584 | 0.9687 | 0.6885 | 0.8531 | 0.1799 | 0.3123 | 0.7385 | 0.8053 | 0.1052 | 0.231 | 0.0247 | 0.1875 | 0.0141 | 0.0788 | 0.0 | 0.0 | 0.1174 | 0.2513 | 0.0 | 0.0 |
| 20.8095 | 8.0 | 1240 | 24.1177 | 0.2921 | 0.4443 | 0.295 | 0.1271 | 0.3495 | 0.8291 | 0.1924 | 0.3698 | 0.3832 | 0.2103 | 0.4345 | 0.9408 | 0.9619 | 0.9723 | 0.6979 | 0.8519 | 0.214 | 0.3559 | 0.7118 | 0.7947 | 0.1017 | 0.2483 | 0.034 | 0.1708 | 0.0059 | 0.0758 | 0.0515 | 0.05 | 0.1278 | 0.2526 | 0.0142 | 0.06 |
| 19.6702 | 9.0 | 1395 | 24.5206 | 0.2803 | 0.4177 | 0.277 | 0.123 | 0.3215 | 0.7562 | 0.181 | 0.3631 | 0.3817 | 0.2104 | 0.4352 | 0.9237 | 0.9632 | 0.9711 | 0.7038 | 0.8569 | 0.2199 | 0.3419 | 0.6239 | 0.7579 | 0.0982 | 0.2724 | 0.0263 | 0.1667 | 0.005 | 0.0818 | 0.0 | 0.0 | 0.1349 | 0.2679 | 0.0276 | 0.1 |
| 18.6977 | 10.0 | 1550 | 24.9112 | 0.2883 | 0.4168 | 0.2833 | 0.1263 | 0.3228 | 0.7868 | 0.1931 | 0.3716 | 0.3891 | 0.2192 | 0.4104 | 0.89 | 0.9602 | 0.9699 | 0.7008 | 0.8541 | 0.2203 | 0.3536 | 0.7002 | 0.8053 | 0.1182 | 0.2741 | 0.0324 | 0.1861 | 0.0067 | 0.0788 | 0.0224 | 0.1 | 0.1209 | 0.2487 | 0.0008 | 0.02 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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