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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


	---
	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