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	---
	language:
	- eng
	license: cc0-1.0
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs
	model-index:
	- name: drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs
	results: []
	---

	drone-DinoVdeau-from-binary is a fine-tuned version of [drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.4061
	- RMSE: 0.2019
	- MAE: 0.1446
	- KL Divergence: 0.9802

	---

	# Model description
	drone-DinoVdeau-from-binary is a model built on top of drone-DinoVdeau-from-binary-large-2024_11_14-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 \| 1272 \| 394 \| 391 \| 2057 \|
	\| Acropore_digitised \| 624 \| 223 \| 217 \| 1064 \|
	\| Acropore_tabular \| 344 \| 144 \| 125 \| 613 \|
	\| Algae \| 5236 \| 1580 \| 1617 \| 8433 \|
	\| Dead_coral \| 2251 \| 650 \| 655 \| 3556 \|
	\| Millepore \| 233 \| 96 \| 97 \| 426 \|
	\| No_acropore_encrusting \| 802 \| 266 \| 285 \| 1353 \|
	\| No_acropore_massive \| 2381 \| 826 \| 822 \| 4029 \|
	\| No_acropore_sub_massive \| 2020 \| 625 \| 651 \| 3296 \|
	\| Rock \| 6151 \| 2004 \| 2004 \| 10159 \|
	\| Rubble \| 5170 \| 1648 \| 1627 \| 8445 \|
	\| Sand \| 6121 \| 2019 \| 1978 \| 10118 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 62.0
	- 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 \| KL div \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.43063807487487793 \| 0.1621 \| 0.2210 \| 1.0069 \| 0.001
	2 \| 0.4245865046977997 \| 0.1547 \| 0.2179 \| 1.3119 \| 0.001
	3 \| 0.422325998544693 \| 0.1554 \| 0.2158 \| 1.0982 \| 0.001
	4 \| 0.41912660002708435 \| 0.1552 \| 0.2142 \| 1.0414 \| 0.001
	5 \| 0.41713497042655945 \| 0.1541 \| 0.2123 \| 1.0698 \| 0.001
	6 \| 0.42093637585639954 \| 0.1520 \| 0.2140 \| 1.1959 \| 0.001
	7 \| 0.4166290760040283 \| 0.1530 \| 0.2126 \| 1.1709 \| 0.001
	8 \| 0.41946443915367126 \| 0.1556 \| 0.2143 \| 0.9712 \| 0.001
	9 \| 0.41668570041656494 \| 0.1524 \| 0.2121 \| 1.1432 \| 0.001
	10 \| 0.4186115860939026 \| 0.1535 \| 0.2139 \| 0.9121 \| 0.001
	11 \| 0.41557687520980835 \| 0.1536 \| 0.2114 \| 0.9950 \| 0.001
	12 \| 0.41883811354637146 \| 0.1555 \| 0.2139 \| 1.0106 \| 0.001
	13 \| 0.41630858182907104 \| 0.1553 \| 0.2121 \| 1.1482 \| 0.001
	14 \| 0.4193180799484253 \| 0.1546 \| 0.2138 \| 1.2111 \| 0.001
	15 \| 0.416218638420105 \| 0.1542 \| 0.2121 \| 1.2043 \| 0.001
	16 \| 0.41389620304107666 \| 0.1528 \| 0.2102 \| 1.0828 \| 0.001
	17 \| 0.4171081781387329 \| 0.1564 \| 0.2118 \| 1.0006 \| 0.001
	18 \| 0.4146382212638855 \| 0.1507 \| 0.2107 \| 1.0514 \| 0.001
	19 \| 0.41857486963272095 \| 0.1532 \| 0.2114 \| 0.9575 \| 0.001
	20 \| 0.41434723138809204 \| 0.1513 \| 0.2108 \| 1.1648 \| 0.001
	21 \| 0.4195358157157898 \| 0.1533 \| 0.2123 \| 1.2950 \| 0.001
	22 \| 0.4339658319950104 \| 0.1524 \| 0.2110 \| inf \| 0.001
	23 \| 0.43265336751937866 \| 0.1517 \| 0.2085 \| nan \| 0.0001
	24 \| 0.4384593963623047 \| 0.1493 \| 0.2092 \| nan \| 0.0001
	25 \| 0.4271779954433441 \| 0.1490 \| 0.2074 \| inf \| 0.0001
	26 \| 0.41048941016197205 \| 0.1480 \| 0.2075 \| 1.1903 \| 0.0001
	27 \| 0.4096038341522217 \| 0.1494 \| 0.2067 \| 0.9915 \| 0.0001
	28 \| 0.4104350507259369 \| 0.1493 \| 0.2075 \| 0.9669 \| 0.0001
	29 \| 0.40966179966926575 \| 0.1469 \| 0.2069 \| 1.0433 \| 0.0001
	30 \| 0.4094092547893524 \| 0.1490 \| 0.2065 \| 0.9082 \| 0.0001
	31 \| 0.40909385681152344 \| 0.1470 \| 0.2065 \| 1.0120 \| 0.0001
	32 \| 0.4084269404411316 \| 0.1483 \| 0.2060 \| 0.9708 \| 0.0001
	33 \| 0.40824124217033386 \| 0.1474 \| 0.2057 \| 0.9317 \| 0.0001
	34 \| 0.40851354598999023 \| 0.1481 \| 0.2061 \| 0.9619 \| 0.0001
	35 \| 0.4072923958301544 \| 0.1466 \| 0.2054 \| 1.0523 \| 0.0001
	36 \| 0.40741708874702454 \| 0.1460 \| 0.2052 \| 1.0622 \| 0.0001
	37 \| 0.40657544136047363 \| 0.1456 \| 0.2047 \| 1.0201 \| 0.0001
	38 \| 0.406360387802124 \| 0.1459 \| 0.2045 \| 1.0557 \| 0.0001
	39 \| 0.4077896773815155 \| 0.1469 \| 0.2056 \| 1.0055 \| 0.0001
	40 \| 0.4068063199520111 \| 0.1464 \| 0.2049 \| 0.9849 \| 0.0001
	41 \| 0.40890073776245117 \| 0.1489 \| 0.2063 \| 0.8999 \| 0.0001
	42 \| 0.4068816602230072 \| 0.1463 \| 0.2049 \| 1.0617 \| 0.0001
	43 \| 0.40578988194465637 \| 0.1450 \| 0.2041 \| 1.0520 \| 0.0001
	44 \| 0.4070681035518646 \| 0.1475 \| 0.2050 \| 1.0054 \| 0.0001
	45 \| 0.40669572353363037 \| 0.1440 \| 0.2047 \| 1.1386 \| 0.0001
	46 \| 0.40670666098594666 \| 0.1457 \| 0.2047 \| 1.0253 \| 0.0001
	47 \| 0.4062415659427643 \| 0.1473 \| 0.2043 \| 1.0430 \| 0.0001
	48 \| 0.4064981937408447 \| 0.1457 \| 0.2048 \| 1.1041 \| 0.0001
	49 \| 0.40709760785102844 \| 0.1463 \| 0.2052 \| 1.0702 \| 0.0001
	50 \| 0.40644556283950806 \| 0.1479 \| 0.2042 \| 0.8917 \| 1e-05
	51 \| 0.40579161047935486 \| 0.1437 \| 0.2041 \| 0.9960 \| 1e-05
	52 \| 0.40528106689453125 \| 0.1446 \| 0.2037 \| 1.0567 \| 1e-05
	53 \| 0.4056229293346405 \| 0.1462 \| 0.2039 \| 1.0205 \| 1e-05
	54 \| 0.4058997631072998 \| 0.1441 \| 0.2041 \| 0.9905 \| 1e-05
	55 \| 0.4060685932636261 \| 0.1471 \| 0.2041 \| 0.9379 \| 1e-05
	56 \| 0.40592971444129944 \| 0.1454 \| 0.2041 \| 0.9696 \| 1e-05
	57 \| 0.4058408737182617 \| 0.1460 \| 0.2041 \| 1.0591 \| 1e-05
	58 \| 0.4063320457935333 \| 0.1460 \| 0.2043 \| 0.9276 \| 1e-05
	59 \| 0.4056239724159241 \| 0.1453 \| 0.2038 \| 0.9794 \| 1.0000000000000002e-06
	60 \| 0.40571752190589905 \| 0.1446 \| 0.2040 \| 1.0349 \| 1.0000000000000002e-06
	61 \| 0.4058452248573303 \| 0.1449 \| 0.2041 \| 0.9860 \| 1.0000000000000002e-06
	62 \| 0.4054276943206787 \| 0.1446 \| 0.2037 \| 0.9528 \| 1.0000000000000002e-06


	---

	# 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-from-binary-large-2024_11_14-batch-size16_freeze_probs
	model-index:
	- name: drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs
	results: []
	---

	drone-DinoVdeau-from-binary is a fine-tuned version of [drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.4061
	- RMSE: 0.2019
	- MAE: 0.1446
	- KL Divergence: 0.9802

	---

	# Model description
	drone-DinoVdeau-from-binary is a model built on top of drone-DinoVdeau-from-binary-large-2024_11_14-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 \| 1272 \| 394 \| 391 \| 2057 \|
	\| Acropore_digitised \| 624 \| 223 \| 217 \| 1064 \|
	\| Acropore_tabular \| 344 \| 144 \| 125 \| 613 \|
	\| Algae \| 5236 \| 1580 \| 1617 \| 8433 \|
	\| Dead_coral \| 2251 \| 650 \| 655 \| 3556 \|
	\| Millepore \| 233 \| 96 \| 97 \| 426 \|
	\| No_acropore_encrusting \| 802 \| 266 \| 285 \| 1353 \|
	\| No_acropore_massive \| 2381 \| 826 \| 822 \| 4029 \|
	\| No_acropore_sub_massive \| 2020 \| 625 \| 651 \| 3296 \|
	\| Rock \| 6151 \| 2004 \| 2004 \| 10159 \|
	\| Rubble \| 5170 \| 1648 \| 1627 \| 8445 \|
	\| Sand \| 6121 \| 2019 \| 1978 \| 10118 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 62.0
	- 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 \| KL div \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.43063807487487793 \| 0.1621 \| 0.2210 \| 1.0069 \| 0.001
	2 \| 0.4245865046977997 \| 0.1547 \| 0.2179 \| 1.3119 \| 0.001
	3 \| 0.422325998544693 \| 0.1554 \| 0.2158 \| 1.0982 \| 0.001
	4 \| 0.41912660002708435 \| 0.1552 \| 0.2142 \| 1.0414 \| 0.001
	5 \| 0.41713497042655945 \| 0.1541 \| 0.2123 \| 1.0698 \| 0.001
	6 \| 0.42093637585639954 \| 0.1520 \| 0.2140 \| 1.1959 \| 0.001
	7 \| 0.4166290760040283 \| 0.1530 \| 0.2126 \| 1.1709 \| 0.001
	8 \| 0.41946443915367126 \| 0.1556 \| 0.2143 \| 0.9712 \| 0.001
	9 \| 0.41668570041656494 \| 0.1524 \| 0.2121 \| 1.1432 \| 0.001
	10 \| 0.4186115860939026 \| 0.1535 \| 0.2139 \| 0.9121 \| 0.001
	11 \| 0.41557687520980835 \| 0.1536 \| 0.2114 \| 0.9950 \| 0.001
	12 \| 0.41883811354637146 \| 0.1555 \| 0.2139 \| 1.0106 \| 0.001
	13 \| 0.41630858182907104 \| 0.1553 \| 0.2121 \| 1.1482 \| 0.001
	14 \| 0.4193180799484253 \| 0.1546 \| 0.2138 \| 1.2111 \| 0.001
	15 \| 0.416218638420105 \| 0.1542 \| 0.2121 \| 1.2043 \| 0.001
	16 \| 0.41389620304107666 \| 0.1528 \| 0.2102 \| 1.0828 \| 0.001
	17 \| 0.4171081781387329 \| 0.1564 \| 0.2118 \| 1.0006 \| 0.001
	18 \| 0.4146382212638855 \| 0.1507 \| 0.2107 \| 1.0514 \| 0.001
	19 \| 0.41857486963272095 \| 0.1532 \| 0.2114 \| 0.9575 \| 0.001
	20 \| 0.41434723138809204 \| 0.1513 \| 0.2108 \| 1.1648 \| 0.001
	21 \| 0.4195358157157898 \| 0.1533 \| 0.2123 \| 1.2950 \| 0.001
	22 \| 0.4339658319950104 \| 0.1524 \| 0.2110 \| inf \| 0.001
	23 \| 0.43265336751937866 \| 0.1517 \| 0.2085 \| nan \| 0.0001
	24 \| 0.4384593963623047 \| 0.1493 \| 0.2092 \| nan \| 0.0001
	25 \| 0.4271779954433441 \| 0.1490 \| 0.2074 \| inf \| 0.0001
	26 \| 0.41048941016197205 \| 0.1480 \| 0.2075 \| 1.1903 \| 0.0001
	27 \| 0.4096038341522217 \| 0.1494 \| 0.2067 \| 0.9915 \| 0.0001
	28 \| 0.4104350507259369 \| 0.1493 \| 0.2075 \| 0.9669 \| 0.0001
	29 \| 0.40966179966926575 \| 0.1469 \| 0.2069 \| 1.0433 \| 0.0001
	30 \| 0.4094092547893524 \| 0.1490 \| 0.2065 \| 0.9082 \| 0.0001
	31 \| 0.40909385681152344 \| 0.1470 \| 0.2065 \| 1.0120 \| 0.0001
	32 \| 0.4084269404411316 \| 0.1483 \| 0.2060 \| 0.9708 \| 0.0001
	33 \| 0.40824124217033386 \| 0.1474 \| 0.2057 \| 0.9317 \| 0.0001
	34 \| 0.40851354598999023 \| 0.1481 \| 0.2061 \| 0.9619 \| 0.0001
	35 \| 0.4072923958301544 \| 0.1466 \| 0.2054 \| 1.0523 \| 0.0001
	36 \| 0.40741708874702454 \| 0.1460 \| 0.2052 \| 1.0622 \| 0.0001
	37 \| 0.40657544136047363 \| 0.1456 \| 0.2047 \| 1.0201 \| 0.0001
	38 \| 0.406360387802124 \| 0.1459 \| 0.2045 \| 1.0557 \| 0.0001
	39 \| 0.4077896773815155 \| 0.1469 \| 0.2056 \| 1.0055 \| 0.0001
	40 \| 0.4068063199520111 \| 0.1464 \| 0.2049 \| 0.9849 \| 0.0001
	41 \| 0.40890073776245117 \| 0.1489 \| 0.2063 \| 0.8999 \| 0.0001
	42 \| 0.4068816602230072 \| 0.1463 \| 0.2049 \| 1.0617 \| 0.0001
	43 \| 0.40578988194465637 \| 0.1450 \| 0.2041 \| 1.0520 \| 0.0001
	44 \| 0.4070681035518646 \| 0.1475 \| 0.2050 \| 1.0054 \| 0.0001
	45 \| 0.40669572353363037 \| 0.1440 \| 0.2047 \| 1.1386 \| 0.0001
	46 \| 0.40670666098594666 \| 0.1457 \| 0.2047 \| 1.0253 \| 0.0001
	47 \| 0.4062415659427643 \| 0.1473 \| 0.2043 \| 1.0430 \| 0.0001
	48 \| 0.4064981937408447 \| 0.1457 \| 0.2048 \| 1.1041 \| 0.0001
	49 \| 0.40709760785102844 \| 0.1463 \| 0.2052 \| 1.0702 \| 0.0001
	50 \| 0.40644556283950806 \| 0.1479 \| 0.2042 \| 0.8917 \| 1e-05
	51 \| 0.40579161047935486 \| 0.1437 \| 0.2041 \| 0.9960 \| 1e-05
	52 \| 0.40528106689453125 \| 0.1446 \| 0.2037 \| 1.0567 \| 1e-05
	53 \| 0.4056229293346405 \| 0.1462 \| 0.2039 \| 1.0205 \| 1e-05
	54 \| 0.4058997631072998 \| 0.1441 \| 0.2041 \| 0.9905 \| 1e-05
	55 \| 0.4060685932636261 \| 0.1471 \| 0.2041 \| 0.9379 \| 1e-05
	56 \| 0.40592971444129944 \| 0.1454 \| 0.2041 \| 0.9696 \| 1e-05
	57 \| 0.4058408737182617 \| 0.1460 \| 0.2041 \| 1.0591 \| 1e-05
	58 \| 0.4063320457935333 \| 0.1460 \| 0.2043 \| 0.9276 \| 1e-05
	59 \| 0.4056239724159241 \| 0.1453 \| 0.2038 \| 0.9794 \| 1.0000000000000002e-06
	60 \| 0.40571752190589905 \| 0.1446 \| 0.2040 \| 1.0349 \| 1.0000000000000002e-06
	61 \| 0.4058452248573303 \| 0.1449 \| 0.2041 \| 0.9860 \| 1.0000000000000002e-06
	62 \| 0.4054276943206787 \| 0.1446 \| 0.2037 \| 0.9528 \| 1.0000000000000002e-06


	---

	# Framework Versions

	- Transformers: 4.41.0
	- Pytorch: 2.5.0+cu124
	- Datasets: 3.0.2
	- Tokenizers: 0.19.1