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---
language:
- eng
license: wtfpl
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: microsoft/resnet-50
model-index:
- name: Resneteau-50-2024_09_23-batch-size32_freeze
results: []
---
Resneteau is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50). It achieves the following results on the test set:
- Loss: 0.1906
- F1 Micro: 0.6954
- F1 Macro: 0.4462
- Accuracy: 0.1827
---
# Model description
Resneteau is a model built on top of microsoft/resnet-50 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 number of images for each class are given in the following table:
| Class | train | val | test | Total |
|:-------------------------|--------:|------:|-------:|--------:|
| Acropore_branched | 1469 | 464 | 475 | 2408 |
| Acropore_digitised | 568 | 160 | 160 | 888 |
| Acropore_sub_massive | 150 | 50 | 43 | 243 |
| Acropore_tabular | 999 | 297 | 293 | 1589 |
| Algae_assembly | 2546 | 847 | 845 | 4238 |
| Algae_drawn_up | 367 | 126 | 127 | 620 |
| Algae_limestone | 1652 | 557 | 563 | 2772 |
| Algae_sodding | 3148 | 984 | 985 | 5117 |
| Atra/Leucospilota | 1084 | 348 | 360 | 1792 |
| Bleached_coral | 219 | 71 | 70 | 360 |
| Blurred | 191 | 67 | 62 | 320 |
| Dead_coral | 1979 | 642 | 643 | 3264 |
| Fish | 2018 | 656 | 647 | 3321 |
| Homo_sapiens | 161 | 62 | 59 | 282 |
| Human_object | 157 | 58 | 55 | 270 |
| Living_coral | 406 | 154 | 141 | 701 |
| Millepore | 385 | 127 | 125 | 637 |
| No_acropore_encrusting | 441 | 130 | 154 | 725 |
| No_acropore_foliaceous | 204 | 36 | 46 | 286 |
| No_acropore_massive | 1031 | 336 | 338 | 1705 |
| No_acropore_solitary | 202 | 53 | 48 | 303 |
| No_acropore_sub_massive | 1401 | 433 | 422 | 2256 |
| Rock | 4489 | 1495 | 1473 | 7457 |
| Rubble | 3092 | 1030 | 1001 | 5123 |
| Sand | 5842 | 1939 | 1938 | 9719 |
| Sea_cucumber | 1408 | 439 | 447 | 2294 |
| Sea_urchins | 327 | 107 | 111 | 545 |
| Sponge | 269 | 96 | 105 | 470 |
| Syringodium_isoetifolium | 1212 | 392 | 391 | 1995 |
| Thalassodendron_ciliatum | 782 | 261 | 260 | 1303 |
| Useless | 579 | 193 | 193 | 965 |
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 28.0
- **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 | Accuracy | F1 Macro | F1 Micro | Learning Rate
--- | --- | --- | --- | --- | ---
1 | 0.24598382413387299 | 0.08766458766458766 | 0.5801698557249565 | 0.226738844317642 | 0.001
2 | 0.22168199717998505 | 0.13686763686763687 | 0.6411905904944791 | 0.3160165508599939 | 0.001
3 | 0.21166761219501495 | 0.14864864864864866 | 0.6595584072466503 | 0.3580673052862397 | 0.001
4 | 0.20492619276046753 | 0.16181566181566182 | 0.6673936750272628 | 0.3831121485565155 | 0.001
5 | 0.20162147283554077 | 0.1677061677061677 | 0.6707461695365495 | 0.3964602797407069 | 0.001
6 | 0.20019273459911346 | 0.1677061677061677 | 0.6719734660033168 | 0.40758628553731013 | 0.001
7 | 0.19761690497398376 | 0.17463617463617465 | 0.6751762240426747 | 0.4142080471846538 | 0.001
8 | 0.19706940650939941 | 0.17636867636867637 | 0.6823529411764706 | 0.42809095916498113 | 0.001
9 | 0.19613835215568542 | 0.17636867636867637 | 0.6844589857443328 | 0.43000179684162393 | 0.001
10 | 0.19443827867507935 | 0.18052668052668053 | 0.676261056657901 | 0.4264062108185488 | 0.001
11 | 0.19399969279766083 | 0.1781011781011781 | 0.6902341199514971 | 0.43914447135579204 | 0.001
12 | 0.19451384246349335 | 0.1729036729036729 | 0.6938511326860841 | 0.45234247782022446 | 0.001
13 | 0.19363747537136078 | 0.1794871794871795 | 0.6907971453892439 | 0.44605482120784584 | 0.001
14 | 0.1931454837322235 | 0.1781011781011781 | 0.6916442548455903 | 0.44244925103284655 | 0.001
15 | 0.1935158371925354 | 0.18087318087318088 | 0.6936180088187515 | 0.44307178033824657 | 0.001
16 | 0.19309590756893158 | 0.18052668052668053 | 0.6895936942854461 | 0.4428841041517678 | 0.001
17 | 0.19311168789863586 | 0.18191268191268192 | 0.6953186376449928 | 0.4411042424961882 | 0.001
18 | 0.19081147015094757 | 0.18572418572418573 | 0.6983818770226538 | 0.4490480976278912 | 0.001
19 | 0.19249168038368225 | 0.1812196812196812 | 0.6878854936673101 | 0.4428453523216445 | 0.001
20 | 0.19134406745433807 | 0.1774081774081774 | 0.6796580216840999 | 0.43568338344914237 | 0.001
21 | 0.19149190187454224 | 0.18225918225918225 | 0.6957772621809745 | 0.4381469652060519 | 0.001
22 | 0.19192616641521454 | 0.1826056826056826 | 0.7038712011577424 | 0.4534807464842353 | 0.001
23 | 0.19255639612674713 | 0.17983367983367984 | 0.6907461850762985 | 0.4363028843794499 | 0.001
24 | 0.19186602532863617 | 0.18052668052668053 | 0.6952745610758312 | 0.45443118252910614 | 0.001
25 | 0.19193170964717865 | 0.1781011781011781 | 0.6961779911373708 | 0.4465566917300777 | 0.0001
26 | 0.19118554890155792 | 0.18225918225918225 | 0.6942802624842929 | 0.441825214268795 | 0.0001
27 | 0.19123922288417816 | 0.18087318087318088 | 0.6971996137398262 | 0.449975636684123 | 0.0001
28 | 0.19151046872138977 | 0.18572418572418573 | 0.6943913469159402 | 0.44543509037683293 | 0.0001
---
# CO2 Emissions
The estimated CO2 emissions for training this model are documented below:
- **Emissions**: 0.1871415951855612 grams of CO2
- **Source**: Code Carbon
- **Training Type**: fine-tuning
- **Geographical Location**: Brest, France
- **Hardware Used**: NVIDIA Tesla V100 PCIe 32 Go
---
# Framework Versions
- **Transformers**: 4.44.2
- **Pytorch**: 2.4.1+cu121
- **Datasets**: 3.0.0
- **Tokenizers**: 0.19.1
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