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---
language:
- eng
license: cc0-1.0
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs
model-index:
- name: drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs
results: []
---
drone-DinoVdeau-produttoria-probabilities is a fine-tuned version of [drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs](https://huggingface.co/drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs). It achieves the following results on the test set:
- Loss: 0.3261
- F1 Micro: 0.8621
- F1 Macro: 0.8264
- Accuracy: 0.1682
- RMSE: 0.2445
- MAE: 0.1621
- R2: 0.4057
| Class | F1 per class |
|----------|-------|
| Acropore_branched | 0.8063 |
| Acropore_digitised | 0.7335 |
| Acropore_tabular | 0.6247 |
| Algae | 0.9859 |
| Dead_coral | 0.8424 |
| Fish | 0.7464 |
| Millepore | 0.6453 |
| No_acropore_encrusting | 0.7292 |
| No_acropore_massive | 0.8681 |
| No_acropore_sub_massive | 0.8092 |
| Rock | 0.9925 |
| Rubble | 0.9693 |
| Sand | 0.9904 |
---
# Model description
drone-DinoVdeau-produttoria-probabilities is a model built on top of drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_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 | 2028 | 684 | 686 | 3398 |
| Acropore_digitised | 2006 | 735 | 717 | 3458 |
| Acropore_tabular | 1237 | 461 | 451 | 2149 |
| Algae | 11086 | 3671 | 3675 | 18432 |
| Dead_coral | 6354 | 2161 | 2147 | 10662 |
| Fish | 4032 | 1430 | 1430 | 6892 |
| Millepore | 1943 | 783 | 772 | 3498 |
| No_acropore_encrusting | 2663 | 986 | 957 | 4606 |
| No_acropore_massive | 6897 | 2375 | 2375 | 11647 |
| No_acropore_sub_massive | 5416 | 1988 | 1958 | 9362 |
| Rock | 11164 | 3726 | 3725 | 18615 |
| Rubble | 10687 | 3570 | 3572 | 17829 |
| Sand | 11151 | 3726 | 3723 | 18600 |
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 45.0
- **Learning Rate**: 0.001
- **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**: 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
--- | --- | --- | --- | --- | ---
0 | N/A | 0.0000 | 0.0000 | 0.0000 | 0.001
1 | 0.36246591806411743 | 0.1880 | 0.2669 | 0.2744 | 0.001
2 | 0.3457428216934204 | 0.1685 | 0.2560 | 0.3367 | 0.001
3 | 0.3518487811088562 | 0.1747 | 0.2597 | 0.3157 | 0.001
4 | 0.3507988750934601 | 0.1751 | 0.2563 | 0.3345 | 0.001
5 | 0.3436409533023834 | 0.1696 | 0.2546 | 0.3371 | 0.001
6 | 0.35096481442451477 | 0.1767 | 0.2598 | 0.3175 | 0.001
7 | 0.3412320613861084 | 0.1750 | 0.2538 | 0.3471 | 0.001
8 | 0.3456409275531769 | 0.1678 | 0.2561 | 0.3435 | 0.001
9 | 0.3425351679325104 | 0.1741 | 0.2545 | 0.3409 | 0.001
10 | 0.33964109420776367 | 0.1711 | 0.2525 | 0.3583 | 0.001
11 | 0.34479108452796936 | 0.1721 | 0.2542 | 0.3498 | 0.001
12 | 0.3415849804878235 | 0.1767 | 0.2527 | 0.3577 | 0.001
13 | 0.33990854024887085 | 0.1677 | 0.2527 | 0.3523 | 0.001
14 | 0.34520208835601807 | 0.1746 | 0.2540 | 0.3443 | 0.001
15 | 0.34849879145622253 | 0.1801 | 0.2568 | 0.3333 | 0.001
16 | 0.34347954392433167 | 0.1718 | 0.2537 | 0.3473 | 0.001
17 | 0.341246634721756 | 0.1711 | 0.2508 | 0.3633 | 0.0001
18 | 0.3398562967777252 | 0.1708 | 0.2507 | 0.3649 | 0.0001
19 | 0.3332718312740326 | 0.1675 | 0.2483 | 0.3775 | 0.0001
20 | 0.333162784576416 | 0.1688 | 0.2478 | 0.3810 | 0.0001
21 | 0.3324449062347412 | 0.1673 | 0.2476 | 0.3810 | 0.0001
22 | 0.3320053517818451 | 0.1671 | 0.2472 | 0.3836 | 0.0001
23 | 0.3301050662994385 | 0.1658 | 0.2461 | 0.3890 | 0.0001
24 | 0.3298528492450714 | 0.1648 | 0.2458 | 0.3899 | 0.0001
25 | 0.32962867617607117 | 0.1641 | 0.2458 | 0.3903 | 0.0001
26 | 0.32889437675476074 | 0.1632 | 0.2454 | 0.3926 | 0.0001
27 | 0.33042922616004944 | 0.1674 | 0.2461 | 0.3891 | 0.0001
28 | 0.32880541682243347 | 0.1645 | 0.2451 | 0.3955 | 0.0001
29 | 0.3293789327144623 | 0.1656 | 0.2451 | 0.3961 | 0.0001
30 | 0.33135533332824707 | 0.1684 | 0.2464 | 0.3914 | 0.0001
31 | 0.32911789417266846 | 0.1608 | 0.2457 | 0.3904 | 0.0001
32 | 0.3289436399936676 | 0.1631 | 0.2453 | 0.3959 | 0.0001
33 | 0.3271527588367462 | 0.1628 | 0.2444 | 0.3972 | 0.0001
34 | 0.32699429988861084 | 0.1621 | 0.2443 | 0.3976 | 0.0001
35 | 0.32638314366340637 | 0.1615 | 0.2439 | 0.3987 | 0.0001
36 | 0.3293066918849945 | 0.1656 | 0.2455 | 0.3946 | 0.0001
37 | 0.3271186649799347 | 0.1597 | 0.2442 | 0.3996 | 0.0001
38 | 0.32695677876472473 | 0.1613 | 0.2437 | 0.4022 | 0.0001
39 | 0.33263665437698364 | 0.1575 | 0.2438 | 0.4007 | 0.0001
40 | 0.33278176188468933 | 0.1651 | 0.2442 | 0.4003 | 0.0001
41 | 0.33069443702697754 | 0.1627 | 0.2435 | 0.4031 | 0.0001
42 | 0.3310275375843048 | 0.1641 | 0.2436 | 0.4030 | 1e-05
43 | 0.32956016063690186 | 0.1603 | 0.2429 | 0.4052 | 1e-05
44 | 0.33022987842559814 | 0.1625 | 0.2432 | 0.4038 | 1e-05
45 | 0.3266430199146271 | 0.1617 | 0.2430 | 0.4047 | 1e-05
---
# Framework Versions
- **Transformers**: 4.41.0
- **Pytorch**: 2.5.0+cu124
- **Datasets**: 3.0.2
- **Tokenizers**: 0.19.1
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