README.md

---
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](https://huggingface.co/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](https://github.com/SeatizenDOI/DinoVdeau).

- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)

---

# 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