SegForCoral-b2-2025_06_03_30567-bs16_refine is a fine-tuned version of
nvidia/mit-b2.
Model description
SegForCoral-b2-2025_06_03_30567-bs16_refine is a model built on top of nvidia/mit-b2 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.
The source code for training the model can be found in this
Git repository.
Intended uses & limitations
You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- Number of Epochs: 32.0
- Learning Rate: 1e-05
- Train Batch Size: 16
- Eval Batch Size: 16
- Optimizer: Adam
- LR Scheduler Type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- Freeze Encoder: No
- Data Augmentation: No
Training results
| Epoch | Validation Loss | Learning Rate |
|---|
| 1 | 0.7078850269317627 | 1e-05 |
| 2 | 0.6609442234039307 | 1e-05 |
| 3 | 0.6220470666885376 | 1e-05 |
| 4 | 0.5949200987815857 | 1e-05 |
| 5 | 0.5844070315361023 | 1e-05 |
| 6 | 0.5725482702255249 | 1e-05 |
| 7 | 0.5633664131164551 | 1e-05 |
| 8 | 0.5647215247154236 | 1e-05 |
| 9 | 0.5570588707923889 | 1e-05 |
| 10 | 0.5523818135261536 | 1e-05 |
| 11 | 0.5505710244178772 | 1e-05 |
| 12 | 0.5467283725738525 | 1e-05 |
| 13 | 0.5548911094665527 | 1e-05 |
| 14 | 0.545318067073822 | 1e-05 |
| 15 | 0.5459777116775513 | 1e-05 |
| 16 | 0.5529848337173462 | 1e-05 |
| 17 | 0.5465011596679688 | 1e-05 |
| 18 | 0.5460110902786255 | 1e-05 |
| 19 | 0.5581657290458679 | 1e-05 |
| 20 | 0.5426208972930908 | 1e-05 |
| 21 | 0.5669962167739868 | 1e-05 |
| 22 | 0.5413931608200073 | 1e-05 |
| 23 | 0.560343325138092 | 1e-05 |
| 24 | 0.5521411895751953 | 1e-05 |
| 25 | 0.5587380528450012 | 1e-05 |
| 26 | 0.5514724850654602 | 1e-05 |
| 27 | 0.552064061164856 | 1e-05 |
| 28 | 0.5605921745300293 | 1e-05 |
| 29 | 0.5458650588989258 | 1.0000000000000002e-06 |
| 30 | 0.5459087491035461 | 1.0000000000000002e-06 |
| 31 | 0.547154426574707 | 1.0000000000000002e-06 |
| 32 | 0.545670747756958 | 1.0000000000000002e-06 |
Test results
📊 Evaluating zone: config/drone_test_polygon_troudeau.geojson
✅ Pixel Accuracy: 0.9297
✅ Mean Accuracy : 0.8092
✅ Mean IoU : 0.5609
Pixel Accuracy Per Class:
- Acropore_branched: 0.8005
- Acropore_tabular: 0.8930
- No_acropore_massive: 0.8727
- No_acropore_sub_massive: 0.5100
- Sand: 0.9695
IoU Per Class:
- Acropore_branched: 0.2471
- Acropore_tabular: 0.5531
- No_acropore_massive: 0.5913
- No_acropore_sub_massive: 0.4544
- Sand: 0.9586
📊 Evaluating zone: config/drone_test_polygon_stleu.geojson
✅ Pixel Accuracy: 0.8016
✅ Mean Accuracy : 0.7548
✅ Mean IoU : 0.5354
Pixel Accuracy Per Class:
- Acropore_branched: 0.7272
- Acropore_tabular: 0.0000
- No_acropore_massive: 0.9735
- No_acropore_sub_massive: 0.3808
- Sand: 0.9376
IoU Per Class:
- Acropore_branched: 0.4750
- Acropore_tabular: 0.0000
- No_acropore_massive: 0.4427
- No_acropore_sub_massive: 0.3150
- Sand: 0.9089
📦 Micro-Averaged Metrics Across Zones (all pixels):
Pixel Accuracy Per Class:
- Acropore_branched: 0.7312
- Acropore_tabular: 0.8930
- No_acropore_massive: 0.9434
- No_acropore_sub_massive: 0.4130
- Sand: 0.9549
IoU Per Class:
- Acropore_branched: 0.4505
- Acropore_tabular: 0.4017
- No_acropore_massive: 0.4757
- No_acropore_sub_massive: 0.3479
- Sand: 0.9356
✅ Pixel Accuracy: 0.8607
✅ Mean Accuracy : 0.7871
✅ Mean IoU : 0.5223
Framework Versions
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
