Training in progress, epoch 1

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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: 0.4693
	- F1 Micro: 0.0000
	- F1 Macro: 0.0000
	- Accuracy: 0.0000
	- RMSE: 0.1576
	- MAE: 0.1172
	- KL Divergence: 0.4185

	---

	# 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 \| 1220 \| 363 \| 362 \| 1945 \|
	\| Acropore_digitised \| 586 \| 195 \| 189 \| 970 \|
	\| Acropore_tabular \| 308 \| 133 \| 119 \| 560 \|
	\| Algae \| 4777 \| 1372 \| 1384 \| 7533 \|
	\| Dead_coral \| 2513 \| 671 \| 693 \| 3877 \|
	\| Millepore \| 136 \| 55 \| 59 \| 250 \|
	\| No_acropore_encrusting \| 252 \| 88 \| 93 \| 433 \|
	\| No_acropore_massive \| 2158 \| 725 \| 726 \| 3609 \|
	\| No_acropore_sub_massive \| 2036 \| 582 \| 612 \| 3230 \|
	\| Rock \| 5976 \| 1941 \| 1928 \| 9845 \|
	\| Rubble \| 4851 \| 1486 \| 1474 \| 7811 \|
	\| Sand \| 6155 \| 2019 \| 1990 \| 10164 \|

	---

	# 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 \| 0.4821413457393646 \| 0.1308 \| 0.1731 \| 0.4219 \| 0.001
	2 \| 0.4784533977508545 \| 0.1263 \| 0.1710 \| 0.6148 \| 0.001
	3 \| 0.47776785492897034 \| 0.1273 \| 0.1699 \| 0.4880 \| 0.001
	4 \| 0.4793245792388916 \| 0.1290 \| 0.1710 \| 0.3418 \| 0.001
	5 \| 0.47521594166755676 \| 0.1280 \| 0.1674 \| 0.3456 \| 0.001
	6 \| 0.478865385055542 \| 0.1254 \| 0.1707 \| 0.6403 \| 0.001
	7 \| 0.4779475927352905 \| 0.1288 \| 0.1709 \| 0.5492 \| 0.001
	8 \| 0.4756968021392822 \| 0.1270 \| 0.1678 \| 0.3383 \| 0.001
	9 \| 0.4731782376766205 \| 0.1231 \| 0.1657 \| 0.5418 \| 0.001
	10 \| 0.4799855649471283 \| 0.1321 \| 0.1723 \| 0.1547 \| 0.001
	11 \| 0.4731641411781311 \| 0.1256 \| 0.1656 \| 0.3437 \| 0.001
	12 \| 0.47767141461372375 \| 0.1293 \| 0.1701 \| 0.2947 \| 0.001
	13 \| 0.48009705543518066 \| 0.1248 \| 0.1677 \| 0.6136 \| 0.001
	14 \| 0.4954195022583008 \| 0.1253 \| 0.1669 \| inf \| 0.001
	15 \| 0.4812238812446594 \| 0.1254 \| 0.1662 \| inf \| 0.001
	16 \| 0.4858487546443939 \| 0.1243 \| 0.1656 \| inf \| 0.0001
	17 \| 0.47084349393844604 \| 0.1223 \| 0.1628 \| 0.4165 \| 0.0001
	18 \| 0.4707622528076172 \| 0.1216 \| 0.1626 \| 0.4066 \| 0.0001
	19 \| 0.47095733880996704 \| 0.1227 \| 0.1632 \| 0.3185 \| 0.0001
	20 \| 0.4696938395500183 \| 0.1205 \| 0.1620 \| 0.4651 \| 0.0001
	21 \| 0.46922874450683594 \| 0.1216 \| 0.1614 \| 0.3773 \| 0.0001
	22 \| 0.4685634672641754 \| 0.1203 \| 0.1609 \| 0.4611 \| 0.0001
	23 \| 0.47018975019454956 \| 0.1226 \| 0.1621 \| 0.2499 \| 0.0001
	24 \| 0.4705464243888855 \| 0.1213 \| 0.1628 \| 0.3702 \| 0.0001
	25 \| 0.4678299129009247 \| 0.1188 \| 0.1601 \| 0.5133 \| 0.0001
	26 \| 0.46802961826324463 \| 0.1179 \| 0.1604 \| 0.5665 \| 0.0001
	27 \| 0.4680938124656677 \| 0.1200 \| 0.1604 \| 0.4242 \| 0.0001
	28 \| 0.4693257212638855 \| 0.1215 \| 0.1616 \| 0.2968 \| 0.0001
	29 \| 0.46847742795944214 \| 0.1197 \| 0.1607 \| 0.3925 \| 0.0001
	30 \| 0.46944141387939453 \| 0.1221 \| 0.1614 \| 0.2495 \| 0.0001
	31 \| 0.4678958058357239 \| 0.1185 \| 0.1601 \| 0.4510 \| 0.0001
	32 \| 0.46778997778892517 \| 0.1193 \| 0.1601 \| 0.3886 \| 1e-05
	33 \| 0.4686955511569977 \| 0.1202 \| 0.1606 \| 0.3132 \| 1e-05
	34 \| 0.46784329414367676 \| 0.1195 \| 0.1601 \| 0.3958 \| 1e-05
	35 \| 0.4671097695827484 \| 0.1180 \| 0.1595 \| 0.4579 \| 1e-05
	36 \| 0.46735426783561707 \| 0.1184 \| 0.1595 \| 0.4391 \| 1e-05
	37 \| 0.468018501996994 \| 0.1191 \| 0.1600 \| 0.3633 \| 1e-05
	38 \| 0.46701580286026 \| 0.1186 \| 0.1592 \| 0.4303 \| 1e-05
	39 \| 0.4673251509666443 \| 0.1187 \| 0.1596 \| 0.4562 \| 1e-05
	40 \| 0.4673212468624115 \| 0.1189 \| 0.1594 \| 0.4065 \| 1e-05
	41 \| 0.4677547216415405 \| 0.1206 \| 0.1599 \| 0.3336 \| 1e-05
	42 \| 0.4671882390975952 \| 0.1178 \| 0.1597 \| 0.5312 \| 1e-05
	43 \| 0.46716412901878357 \| 0.1185 \| 0.1592 \| 0.3924 \| 1e-05
	44 \| 0.4678168296813965 \| 0.1194 \| 0.1602 \| 0.4259 \| 1e-05
	45 \| 0.46699702739715576 \| 0.1172 \| 0.1594 \| 0.5214 \| 1.0000000000000002e-06
	46 \| 0.46712958812713623 \| 0.1188 \| 0.1594 \| 0.4175 \| 1.0000000000000002e-06
	47 \| 0.4666382074356079 \| 0.1188 \| 0.1589 \| 0.4446 \| 1.0000000000000002e-06
	48 \| 0.46714723110198975 \| 0.1180 \| 0.1597 \| 0.5755 \| 1.0000000000000002e-06
	49 \| 0.46758702397346497 \| 0.1192 \| 0.1600 \| 0.4304 \| 1.0000000000000002e-06
	50 \| 0.46752068400382996 \| 0.1204 \| 0.1595 \| 0.3337 \| 1.0000000000000002e-06
	51 \| 0.46691644191741943 \| 0.1181 \| 0.1591 \| 0.3955 \| 1.0000000000000002e-06
	52 \| 0.466439425945282 \| 0.1175 \| 0.1588 \| 0.4761 \| 1.0000000000000002e-06
	53 \| 0.4667709469795227 \| 0.1189 \| 0.1590 \| 0.4327 \| 1.0000000000000002e-06
	54 \| 0.46701404452323914 \| 0.1187 \| 0.1592 \| 0.3725 \| 1.0000000000000002e-06
	55 \| 0.467383474111557 \| 0.1199 \| 0.1595 \| 0.3841 \| 1.0000000000000002e-06
	56 \| 0.46739572286605835 \| 0.1190 \| 0.1596 \| 0.3822 \| 1.0000000000000002e-06
	57 \| 0.46702033281326294 \| 0.1186 \| 0.1593 \| 0.4675 \| 1.0000000000000002e-06
	58 \| 0.46735846996307373 \| 0.1189 \| 0.1596 \| 0.3738 \| 1.0000000000000002e-06
	59 \| 0.46666717529296875 \| 0.1185 \| 0.1589 \| 0.4204 \| 1.0000000000000002e-07
	60 \| 0.46685320138931274 \| 0.1178 \| 0.1592 \| 0.4532 \| 1.0000000000000002e-07
	61 \| 0.46734780073165894 \| 0.1189 \| 0.1596 \| 0.4032 \| 1.0000000000000002e-07
	62 \| 0.4673011302947998 \| 0.1189 \| 0.1595 \| 0.3407 \| 1.0000000000000002e-07


	---

	# 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: 0.4693
	- F1 Micro: 0.0000
	- F1 Macro: 0.0000
	- Accuracy: 0.0000
	- RMSE: 0.1576
	- MAE: 0.1172
	- KL Divergence: 0.4185

	---

	# 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 \| 1220 \| 363 \| 362 \| 1945 \|
	\| Acropore_digitised \| 586 \| 195 \| 189 \| 970 \|
	\| Acropore_tabular \| 308 \| 133 \| 119 \| 560 \|
	\| Algae \| 4777 \| 1372 \| 1384 \| 7533 \|
	\| Dead_coral \| 2513 \| 671 \| 693 \| 3877 \|
	\| Millepore \| 136 \| 55 \| 59 \| 250 \|
	\| No_acropore_encrusting \| 252 \| 88 \| 93 \| 433 \|
	\| No_acropore_massive \| 2158 \| 725 \| 726 \| 3609 \|
	\| No_acropore_sub_massive \| 2036 \| 582 \| 612 \| 3230 \|
	\| Rock \| 5976 \| 1941 \| 1928 \| 9845 \|
	\| Rubble \| 4851 \| 1486 \| 1474 \| 7811 \|
	\| Sand \| 6155 \| 2019 \| 1990 \| 10164 \|

	---

	# 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 \| 0.4821413457393646 \| 0.1308 \| 0.1731 \| 0.4219 \| 0.001
	2 \| 0.4784533977508545 \| 0.1263 \| 0.1710 \| 0.6148 \| 0.001
	3 \| 0.47776785492897034 \| 0.1273 \| 0.1699 \| 0.4880 \| 0.001
	4 \| 0.4793245792388916 \| 0.1290 \| 0.1710 \| 0.3418 \| 0.001
	5 \| 0.47521594166755676 \| 0.1280 \| 0.1674 \| 0.3456 \| 0.001
	6 \| 0.478865385055542 \| 0.1254 \| 0.1707 \| 0.6403 \| 0.001
	7 \| 0.4779475927352905 \| 0.1288 \| 0.1709 \| 0.5492 \| 0.001
	8 \| 0.4756968021392822 \| 0.1270 \| 0.1678 \| 0.3383 \| 0.001
	9 \| 0.4731782376766205 \| 0.1231 \| 0.1657 \| 0.5418 \| 0.001
	10 \| 0.4799855649471283 \| 0.1321 \| 0.1723 \| 0.1547 \| 0.001
	11 \| 0.4731641411781311 \| 0.1256 \| 0.1656 \| 0.3437 \| 0.001
	12 \| 0.47767141461372375 \| 0.1293 \| 0.1701 \| 0.2947 \| 0.001
	13 \| 0.48009705543518066 \| 0.1248 \| 0.1677 \| 0.6136 \| 0.001
	14 \| 0.4954195022583008 \| 0.1253 \| 0.1669 \| inf \| 0.001
	15 \| 0.4812238812446594 \| 0.1254 \| 0.1662 \| inf \| 0.001
	16 \| 0.4858487546443939 \| 0.1243 \| 0.1656 \| inf \| 0.0001
	17 \| 0.47084349393844604 \| 0.1223 \| 0.1628 \| 0.4165 \| 0.0001
	18 \| 0.4707622528076172 \| 0.1216 \| 0.1626 \| 0.4066 \| 0.0001
	19 \| 0.47095733880996704 \| 0.1227 \| 0.1632 \| 0.3185 \| 0.0001
	20 \| 0.4696938395500183 \| 0.1205 \| 0.1620 \| 0.4651 \| 0.0001
	21 \| 0.46922874450683594 \| 0.1216 \| 0.1614 \| 0.3773 \| 0.0001
	22 \| 0.4685634672641754 \| 0.1203 \| 0.1609 \| 0.4611 \| 0.0001
	23 \| 0.47018975019454956 \| 0.1226 \| 0.1621 \| 0.2499 \| 0.0001
	24 \| 0.4705464243888855 \| 0.1213 \| 0.1628 \| 0.3702 \| 0.0001
	25 \| 0.4678299129009247 \| 0.1188 \| 0.1601 \| 0.5133 \| 0.0001
	26 \| 0.46802961826324463 \| 0.1179 \| 0.1604 \| 0.5665 \| 0.0001
	27 \| 0.4680938124656677 \| 0.1200 \| 0.1604 \| 0.4242 \| 0.0001
	28 \| 0.4693257212638855 \| 0.1215 \| 0.1616 \| 0.2968 \| 0.0001
	29 \| 0.46847742795944214 \| 0.1197 \| 0.1607 \| 0.3925 \| 0.0001
	30 \| 0.46944141387939453 \| 0.1221 \| 0.1614 \| 0.2495 \| 0.0001
	31 \| 0.4678958058357239 \| 0.1185 \| 0.1601 \| 0.4510 \| 0.0001
	32 \| 0.46778997778892517 \| 0.1193 \| 0.1601 \| 0.3886 \| 1e-05
	33 \| 0.4686955511569977 \| 0.1202 \| 0.1606 \| 0.3132 \| 1e-05
	34 \| 0.46784329414367676 \| 0.1195 \| 0.1601 \| 0.3958 \| 1e-05
	35 \| 0.4671097695827484 \| 0.1180 \| 0.1595 \| 0.4579 \| 1e-05
	36 \| 0.46735426783561707 \| 0.1184 \| 0.1595 \| 0.4391 \| 1e-05
	37 \| 0.468018501996994 \| 0.1191 \| 0.1600 \| 0.3633 \| 1e-05
	38 \| 0.46701580286026 \| 0.1186 \| 0.1592 \| 0.4303 \| 1e-05
	39 \| 0.4673251509666443 \| 0.1187 \| 0.1596 \| 0.4562 \| 1e-05
	40 \| 0.4673212468624115 \| 0.1189 \| 0.1594 \| 0.4065 \| 1e-05
	41 \| 0.4677547216415405 \| 0.1206 \| 0.1599 \| 0.3336 \| 1e-05
	42 \| 0.4671882390975952 \| 0.1178 \| 0.1597 \| 0.5312 \| 1e-05
	43 \| 0.46716412901878357 \| 0.1185 \| 0.1592 \| 0.3924 \| 1e-05
	44 \| 0.4678168296813965 \| 0.1194 \| 0.1602 \| 0.4259 \| 1e-05
	45 \| 0.46699702739715576 \| 0.1172 \| 0.1594 \| 0.5214 \| 1.0000000000000002e-06
	46 \| 0.46712958812713623 \| 0.1188 \| 0.1594 \| 0.4175 \| 1.0000000000000002e-06
	47 \| 0.4666382074356079 \| 0.1188 \| 0.1589 \| 0.4446 \| 1.0000000000000002e-06
	48 \| 0.46714723110198975 \| 0.1180 \| 0.1597 \| 0.5755 \| 1.0000000000000002e-06
	49 \| 0.46758702397346497 \| 0.1192 \| 0.1600 \| 0.4304 \| 1.0000000000000002e-06
	50 \| 0.46752068400382996 \| 0.1204 \| 0.1595 \| 0.3337 \| 1.0000000000000002e-06
	51 \| 0.46691644191741943 \| 0.1181 \| 0.1591 \| 0.3955 \| 1.0000000000000002e-06
	52 \| 0.466439425945282 \| 0.1175 \| 0.1588 \| 0.4761 \| 1.0000000000000002e-06
	53 \| 0.4667709469795227 \| 0.1189 \| 0.1590 \| 0.4327 \| 1.0000000000000002e-06
	54 \| 0.46701404452323914 \| 0.1187 \| 0.1592 \| 0.3725 \| 1.0000000000000002e-06
	55 \| 0.467383474111557 \| 0.1199 \| 0.1595 \| 0.3841 \| 1.0000000000000002e-06
	56 \| 0.46739572286605835 \| 0.1190 \| 0.1596 \| 0.3822 \| 1.0000000000000002e-06
	57 \| 0.46702033281326294 \| 0.1186 \| 0.1593 \| 0.4675 \| 1.0000000000000002e-06
	58 \| 0.46735846996307373 \| 0.1189 \| 0.1596 \| 0.3738 \| 1.0000000000000002e-06
	59 \| 0.46666717529296875 \| 0.1185 \| 0.1589 \| 0.4204 \| 1.0000000000000002e-07
	60 \| 0.46685320138931274 \| 0.1178 \| 0.1592 \| 0.4532 \| 1.0000000000000002e-07
	61 \| 0.46734780073165894 \| 0.1189 \| 0.1596 \| 0.4032 \| 1.0000000000000002e-07
	62 \| 0.4673011302947998 \| 0.1189 \| 0.1595 \| 0.3407 \| 1.0000000000000002e-07


	---

	# Framework Versions

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