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

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


	- Loss: 0.4672
	- RMSE: 0.1550
	- MAE: 0.1155
	- KL Divergence: 0.3295

	---

	# Model description
	drone-DinoVdeau-from-probs is a model built on top of drone-DinoVdeau-from-probs-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: 94.0
	- 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 \| 0.4933677613735199 \| 0.1294 \| 0.1825 \| 0.9903 \| 0.001
	2 \| 0.47886165976524353 \| 0.1262 \| 0.1716 \| 0.6847 \| 0.001
	3 \| 0.4788369834423065 \| 0.1271 \| 0.1709 \| 0.5498 \| 0.001
	4 \| 0.47656363248825073 \| 0.1278 \| 0.1695 \| 0.3131 \| 0.001
	5 \| 0.4765072166919708 \| 0.1277 \| 0.1687 \| 0.4013 \| 0.001
	6 \| 0.47649845480918884 \| 0.1243 \| 0.1689 \| 0.6370 \| 0.001
	7 \| 0.47629299759864807 \| 0.1292 \| 0.1694 \| 0.4314 \| 0.001
	8 \| 0.4755041003227234 \| 0.1267 \| 0.1681 \| 0.3379 \| 0.001
	9 \| 0.47342246770858765 \| 0.1250 \| 0.1662 \| 0.4916 \| 0.001
	10 \| 0.47546806931495667 \| 0.1277 \| 0.1677 \| 0.3348 \| 0.001
	11 \| 0.4731104075908661 \| 0.1255 \| 0.1659 \| 0.3524 \| 0.001
	12 \| 0.47679492831230164 \| 0.1306 \| 0.1690 \| 0.2383 \| 0.001
	13 \| 0.4736888110637665 \| 0.1223 \| 0.1666 \| 0.6968 \| 0.001
	14 \| 0.4736703634262085 \| 0.1254 \| 0.1658 \| 0.3983 \| 0.001
	15 \| 0.4738818407058716 \| 0.1248 \| 0.1664 \| 0.5620 \| 0.001
	16 \| 0.47202879190444946 \| 0.1231 \| 0.1648 \| 0.6049 \| 0.001
	17 \| 0.47406336665153503 \| 0.1265 \| 0.1664 \| 0.3072 \| 0.001
	18 \| 0.4738321006298065 \| 0.1253 \| 0.1650 \| 0.3350 \| 0.001
	19 \| 0.476326048374176 \| 0.1282 \| 0.1672 \| 0.2746 \| 0.001
	20 \| 0.4755523204803467 \| 0.1245 \| 0.1670 \| 0.5659 \| 0.001
	21 \| 0.47340598702430725 \| 0.1230 \| 0.1662 \| 0.6154 \| 0.001
	22 \| 0.47443991899490356 \| 0.1223 \| 0.1677 \| 0.7974 \| 0.001
	23 \| 0.47205689549446106 \| 0.1252 \| 0.1639 \| 0.2307 \| 0.0001
	24 \| 0.4706146717071533 \| 0.1217 \| 0.1631 \| 0.4219 \| 0.0001
	25 \| 0.46876564621925354 \| 0.1195 \| 0.1612 \| 0.5242 \| 0.0001
	26 \| 0.46925392746925354 \| 0.1190 \| 0.1620 \| 0.6159 \| 0.0001
	27 \| 0.46849024295806885 \| 0.1206 \| 0.1607 \| 0.4046 \| 0.0001
	28 \| 0.46939656138420105 \| 0.1220 \| 0.1616 \| 0.2860 \| 0.0001
	29 \| 0.46892231702804565 \| 0.1197 \| 0.1614 \| 0.4270 \| 0.0001
	30 \| 0.46987923979759216 \| 0.1225 \| 0.1619 \| 0.2625 \| 0.0001
	31 \| 0.46842578053474426 \| 0.1197 \| 0.1607 \| 0.3876 \| 0.0001
	32 \| 0.46784707903862 \| 0.1195 \| 0.1600 \| 0.4060 \| 0.0001
	33 \| 0.46755874156951904 \| 0.1193 \| 0.1596 \| 0.3688 \| 0.0001
	34 \| 0.46766504645347595 \| 0.1194 \| 0.1600 \| 0.3900 \| 0.0001
	35 \| 0.4670174717903137 \| 0.1189 \| 0.1593 \| 0.4282 \| 0.0001
	36 \| 0.46679624915122986 \| 0.1180 \| 0.1591 \| 0.4446 \| 0.0001
	37 \| 0.46689239144325256 \| 0.1185 \| 0.1590 \| 0.3942 \| 0.0001
	38 \| 0.4664570987224579 \| 0.1177 \| 0.1588 \| 0.4783 \| 0.0001
	39 \| 0.4674011468887329 \| 0.1190 \| 0.1597 \| 0.3868 \| 0.0001
	40 \| 0.4677062928676605 \| 0.1195 \| 0.1599 \| 0.3627 \| 0.0001
	41 \| 0.46822381019592285 \| 0.1211 \| 0.1602 \| 0.2655 \| 0.0001
	42 \| 0.4664672613143921 \| 0.1172 \| 0.1589 \| 0.5072 \| 0.0001
	43 \| 0.46638762950897217 \| 0.1177 \| 0.1585 \| 0.4306 \| 0.0001
	44 \| 0.46708741784095764 \| 0.1192 \| 0.1594 \| 0.4115 \| 0.0001
	45 \| 0.46663177013397217 \| 0.1171 \| 0.1590 \| 0.4417 \| 0.0001
	46 \| 0.4663327634334564 \| 0.1179 \| 0.1585 \| 0.3686 \| 0.0001
	47 \| 0.46577510237693787 \| 0.1172 \| 0.1582 \| 0.5090 \| 0.0001
	48 \| 0.46634000539779663 \| 0.1175 \| 0.1589 \| 0.5279 \| 0.0001
	49 \| 0.46656596660614014 \| 0.1183 \| 0.1591 \| 0.4497 \| 0.0001
	50 \| 0.46755433082580566 \| 0.1205 \| 0.1595 \| 0.2712 \| 0.0001
	51 \| 0.46639156341552734 \| 0.1172 \| 0.1586 \| 0.4008 \| 0.0001
	52 \| 0.46591076254844666 \| 0.1163 \| 0.1583 \| 0.4922 \| 0.0001
	53 \| 0.4656851887702942 \| 0.1178 \| 0.1579 \| 0.4274 \| 0.0001
	54 \| 0.46629655361175537 \| 0.1158 \| 0.1585 \| 0.4574 \| 0.0001
	55 \| 0.46644341945648193 \| 0.1189 \| 0.1586 \| 0.3486 \| 0.0001
	56 \| 0.4661739766597748 \| 0.1184 \| 0.1584 \| 0.3016 \| 0.0001
	57 \| 0.46634721755981445 \| 0.1181 \| 0.1587 \| 0.4163 \| 0.0001
	58 \| 0.4673805236816406 \| 0.1189 \| 0.1593 \| 0.3399 \| 0.0001
	59 \| 0.4650005102157593 \| 0.1170 \| 0.1572 \| 0.3686 \| 0.0001
	60 \| 0.46599113941192627 \| 0.1172 \| 0.1584 \| 0.4535 \| 0.0001
	61 \| 0.4662201702594757 \| 0.1179 \| 0.1585 \| 0.3751 \| 0.0001
	62 \| 0.46614503860473633 \| 0.1173 \| 0.1583 \| 0.3534 \| 0.0001
	63 \| 0.4660026431083679 \| 0.1163 \| 0.1583 \| 0.4048 \| 0.0001
	64 \| 0.46711620688438416 \| 0.1188 \| 0.1588 \| 0.2471 \| 0.0001
	65 \| 0.46536803245544434 \| 0.1166 \| 0.1577 \| 0.4526 \| 0.0001
	66 \| 0.46573594212532043 \| 0.1161 \| 0.1582 \| 0.5259 \| 1e-05
	67 \| 0.4653942584991455 \| 0.1173 \| 0.1574 \| 0.4252 \| 1e-05
	68 \| 0.46487176418304443 \| 0.1154 \| 0.1572 \| 0.4989 \| 1e-05
	69 \| 0.465110719203949 \| 0.1161 \| 0.1570 \| 0.4023 \| 1e-05
	70 \| 0.466043084859848 \| 0.1166 \| 0.1576 \| 0.4118 \| 1e-05
	71 \| 0.46608996391296387 \| 0.1177 \| 0.1578 \| 0.3075 \| 1e-05
	72 \| 0.46582332253456116 \| 0.1171 \| 0.1580 \| 0.3836 \| 1e-05
	73 \| 0.4648771584033966 \| 0.1154 \| 0.1569 \| 0.4544 \| 1e-05
	74 \| 0.46466848254203796 \| 0.1163 \| 0.1567 \| 0.4538 \| 1e-05
	75 \| 0.46563470363616943 \| 0.1166 \| 0.1573 \| 0.3348 \| 1e-05
	76 \| 0.4647076725959778 \| 0.1158 \| 0.1571 \| 0.4976 \| 1e-05
	77 \| 0.4650570750236511 \| 0.1163 \| 0.1570 \| 0.3934 \| 1e-05
	78 \| 0.46495845913887024 \| 0.1161 \| 0.1571 \| 0.3936 \| 1e-05
	79 \| 0.46530288457870483 \| 0.1159 \| 0.1573 \| 0.3759 \| 1e-05
	80 \| 0.4647064805030823 \| 0.1162 \| 0.1567 \| 0.4189 \| 1e-05
	81 \| 0.46485888957977295 \| 0.1158 \| 0.1571 \| 0.4751 \| 1.0000000000000002e-06
	82 \| 0.4654049277305603 \| 0.1161 \| 0.1572 \| 0.4335 \| 1.0000000000000002e-06
	83 \| 0.46466442942619324 \| 0.1161 \| 0.1566 \| 0.3906 \| 1.0000000000000002e-06
	84 \| 0.46430692076683044 \| 0.1157 \| 0.1564 \| 0.3855 \| 1.0000000000000002e-06
	85 \| 0.46528080105781555 \| 0.1173 \| 0.1571 \| 0.3372 \| 1.0000000000000002e-06
	86 \| 0.46546733379364014 \| 0.1184 \| 0.1572 \| 0.2969 \| 1.0000000000000002e-06
	87 \| 0.4651782214641571 \| 0.1173 \| 0.1571 \| 0.3572 \| 1.0000000000000002e-06
	88 \| 0.4655611217021942 \| 0.1151 \| 0.1578 \| 0.5179 \| 1.0000000000000002e-06
	89 \| 0.4654468297958374 \| 0.1177 \| 0.1574 \| 0.2948 \| 1.0000000000000002e-06
	90 \| 0.4649873971939087 \| 0.1167 \| 0.1569 \| 0.3427 \| 1.0000000000000002e-06
	91 \| 0.4655340015888214 \| 0.1173 \| 0.1572 \| 0.2790 \| 1.0000000000000002e-07
	92 \| 0.4645555317401886 \| 0.1153 \| 0.1566 \| 0.4153 \| 1.0000000000000002e-07
	93 \| 0.4648568034172058 \| 0.1153 \| 0.1571 \| 0.4664 \| 1.0000000000000002e-07
	94 \| 0.4652610421180725 \| 0.1159 \| 0.1568 \| 0.3859 \| 1.0000000000000002e-07


	---

	# Framework Versions

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