metadata
language:
- eng
license: cc0-1.0
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs
model-index:
- name: Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs
results: []
Ziboiai is a fine-tuned version of Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs. It achieves the following results on the test set:
- Loss: 0.6188
- F1 Micro: 0.9261
- F1 Macro: 0.8546
- Accuracy: 0.1600
- RMSE: 0.3415
- MAE: 0.3061
- R2: -1.5955
| Class | F1 per class |
|---|---|
| Acropore_branched | 0.8966 |
| Acropore_digitised | 0.6301 |
| Acropore_tabular | 1.0000 |
| Algae | 1.0000 |
| Dead_coral | 0.8395 |
| Fish | 0.8861 |
| Millepore | 1.0000 |
| No_acropore_encrusting | 1.0000 |
| No_acropore_massive | 0.0000 |
| No_acropore_sub_massive | 0.8571 |
| Rock | 1.0000 |
| Rubble | 1.0000 |
| Sand | 1.0000 |
Model description
Ziboiai is a model built on top of Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs 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.
- Developed by: lombardata, credits to César Leblanc and Victor Illien
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 and evaluation data
Details on the estimated number of images for each class are given in the following table:
| Class | train | test | val | Total |
|---|---|---|---|---|
| Acropore_branched | 41 | 32 | 33 | 106 |
| Acropore_digitised | 15 | 14 | 14 | 43 |
| Acropore_tabular | 5 | 8 | 7 | 20 |
| Algae | 50 | 50 | 50 | 150 |
| Dead_coral | 25 | 28 | 30 | 83 |
| Fish | 34 | 24 | 31 | 89 |
| Millepore | 1 | 0 | 0 | 1 |
| No_acropore_encrusting | 1 | 0 | 0 | 1 |
| No_acropore_massive | 2 | 5 | 5 | 12 |
| No_acropore_sub_massive | 27 | 28 | 27 | 82 |
| Rock | 45 | 47 | 45 | 137 |
| Rubble | 40 | 45 | 44 | 129 |
| Sand | 42 | 46 | 45 | 133 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- Number of Epochs: 40.0
- Learning Rate: 0.001
- Train Batch Size: 32
- Eval Batch Size: 32
- Optimizer: Adam
- LR Scheduler Type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- Freeze Encoder: Yes
- Data Augmentation: Yes
Data Augmentation
Data were augmented using the following transformations :
Train Transforms
- PreProcess: No additional parameters
- Resize: probability=1.00
- RandomHorizontalFlip: probability=0.25
- RandomVerticalFlip: probability=0.25
- ColorJiggle: probability=0.25
- RandomPerspective: probability=0.25
- Normalize: probability=1.00
Val Transforms
- PreProcess: No additional parameters
- Resize: probability=1.00
- Normalize: probability=1.00
Training results
| Epoch | Validation Loss | MAE | RMSE | R2 | Learning Rate |
|---|---|---|---|---|---|
| 1 | 0.7150455713272095 | 0.3848940134048462 | 0.40997111797332764 | -20.29086685180664 | 0.001 |
| 2 | 0.7314126491546631 | 0.3895121216773987 | 0.4163060486316681 | -21.218204498291016 | 0.001 |
| 3 | 0.7726277112960815 | 0.40413352847099304 | 0.4320966601371765 | -24.822391510009766 | 0.001 |
| 4 | 0.7917326092720032 | 0.4094983637332916 | 0.4379725754261017 | -26.581586837768555 | 0.001 |
| 5 | 0.7852649092674255 | 0.402120441198349 | 0.43184274435043335 | -26.95589256286621 | 0.001 |
| 6 | 0.7647674679756165 | 0.3905399441719055 | 0.42244094610214233 | -24.40153694152832 | 0.001 |
| 7 | 0.7391812205314636 | 0.376028835773468 | 0.41028541326522827 | -22.557889938354492 | 0.001 |
| 8 | 0.7115270495414734 | 0.36385056376457214 | 0.39825379848480225 | -20.067392349243164 | 0.0001 |
| 9 | 0.6896975040435791 | 0.35347798466682434 | 0.3878582715988159 | -18.16646385192871 | 0.0001 |
| 10 | 0.6777035593986511 | 0.34683120250701904 | 0.3818005323410034 | -16.94469451904297 | 0.0001 |
| 11 | 0.6701759099960327 | 0.3423532247543335 | 0.3779585659503937 | -16.037521362304688 | 0.0001 |
| 12 | 0.663905918598175 | 0.3388546407222748 | 0.37438222765922546 | -15.605177879333496 | 0.0001 |
| 13 | 0.656491219997406 | 0.3345881700515747 | 0.3702985942363739 | -14.805088996887207 | 0.0001 |
| 14 | 0.6501385569572449 | 0.33100754022598267 | 0.3668138384819031 | -14.231175422668457 | 0.0001 |
| 15 | 0.6467865705490112 | 0.32885220646858215 | 0.36475783586502075 | -14.07986831665039 | 0.0001 |
| 16 | 0.6471170783042908 | 0.3288896679878235 | 0.3650059998035431 | -14.255745887756348 | 0.0001 |
| 17 | 0.6435126662254333 | 0.3268200755119324 | 0.36310678720474243 | -14.059813499450684 | 0.0001 |
| 18 | 0.6437923908233643 | 0.3269612491130829 | 0.36342939734458923 | -14.036934852600098 | 0.0001 |
| 19 | 0.6399621367454529 | 0.3249860107898712 | 0.36136963963508606 | -13.81522274017334 | 0.0001 |
| 20 | 0.6391971707344055 | 0.3246455192565918 | 0.3608955144882202 | -13.710391998291016 | 0.0001 |
| 21 | 0.6386714577674866 | 0.32462170720100403 | 0.3606450855731964 | -13.809860229492188 | 0.0001 |
| 22 | 0.6388444304466248 | 0.3243348002433777 | 0.36056435108184814 | -13.849721908569336 | 0.0001 |
| 23 | 0.6361631155014038 | 0.3227779269218445 | 0.35895633697509766 | -13.562189102172852 | 0.0001 |
| 24 | 0.635435163974762 | 0.3223152160644531 | 0.35847193002700806 | -13.645319938659668 | 0.0001 |
| 25 | 0.6344550848007202 | 0.32144099473953247 | 0.35783687233924866 | -13.602314949035645 | 0.0001 |
| 26 | 0.6348865628242493 | 0.3211889863014221 | 0.3580625355243683 | -13.630416870117188 | 0.0001 |
| 27 | 0.6332749724388123 | 0.32009246945381165 | 0.3570806384086609 | -13.561347007751465 | 0.0001 |
| 28 | 0.6295092701911926 | 0.31767499446868896 | 0.35479238629341125 | -13.23308277130127 | 0.0001 |
| 29 | 0.6285346746444702 | 0.3173280954360962 | 0.35434553027153015 | -13.162256240844727 | 0.0001 |
| 30 | 0.6263097524642944 | 0.31627562642097473 | 0.3532228171825409 | -12.713174819946289 | 0.0001 |
| 31 | 0.6272528767585754 | 0.316723495721817 | 0.35376670956611633 | -12.873921394348145 | 0.0001 |
| 32 | 0.6294133067131042 | 0.31807586550712585 | 0.3550169765949249 | -12.935453414916992 | 0.0001 |
| 33 | 0.6299176216125488 | 0.3185364603996277 | 0.35538923740386963 | -12.93520736694336 | 0.0001 |
| 34 | 0.6320692300796509 | 0.3193182349205017 | 0.35644862055778503 | -13.267191886901855 | 0.0001 |
| 35 | 0.6279481649398804 | 0.31752488017082214 | 0.3541102707386017 | -12.99951171875 | 0.0001 |
| 36 | 0.6280075907707214 | 0.31736499071121216 | 0.35407301783561707 | -13.00741195678711 | 0.0001 |
| 37 | 0.6303659081459045 | 0.3187006115913391 | 0.35543760657310486 | -13.230977058410645 | 1e-05 |
| 38 | 0.6297122836112976 | 0.31833118200302124 | 0.3550592064857483 | -12.983016967773438 | 1e-05 |
| 39 | 0.630845308303833 | 0.3193325996398926 | 0.35580796003341675 | -13.159842491149902 | 1e-05 |
| 40 | 0.6291573643684387 | 0.3182610869407654 | 0.3547934889793396 | -13.069788932800293 | 1e-05 |
Framework Versions
- Transformers: 4.44.2
- Pytorch: 2.4.1+cu121
- Datasets: 3.0.0
- Tokenizers: 0.19.1