Upload README.md

0da2bb9 verified 4 days ago

13 kB


	---
	language:
	- eng
	license: cc0-1.0
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze
	model-index:
	- name: drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze
	results: []
	---

	drone-DinoVdeau-produttoria_binary-binary is a fine-tuned version of [drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze](https://huggingface.co/drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze). It achieves the following results on the test set:


	- Loss: 0.2854
	- F1 Micro: 0.8468
	- F1 Macro: 0.6351
	- Accuracy: 0.2786

	\| Class \| F1 per class \|
	\|----------\|-------\|
	\| Acropore_branched \| 0.8084 \|
	\| Acropore_digitised \| 0.5125 \|
	\| Acropore_tabular \| 0.3951 \|
	\| Algae \| 0.9562 \|
	\| Dead_coral \| 0.7470 \|
	\| Fish \| 0.6639 \|
	\| Millepore \| 0.3021 \|
	\| No_acropore_encrusting \| 0.5923 \|
	\| No_acropore_massive \| 0.7651 \|
	\| No_acropore_sub_massive \| 0.6345 \|
	\| Rock \| 0.9536 \|
	\| Rubble \| 0.9042 \|
	\| Sand \| 0.9008 \|


	---

	# Model description
	drone-DinoVdeau-produttoria_binary-binary is a model built on top of drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze 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 \| test \| val \| Total \|
	\|:------------------------\|--------:\|-------:\|------:\|--------:\|
	\| Acropore_branched \| 1483 \| 522 \| 529 \| 2534 \|
	\| Acropore_digitised \| 1085 \| 371 \| 362 \| 1818 \|
	\| Acropore_tabular \| 486 \| 176 \| 178 \| 840 \|
	\| Algae \| 10340 \| 3441 \| 3461 \| 17242 \|
	\| Dead_coral \| 3710 \| 1252 \| 1267 \| 6229 \|
	\| Fish \| 1462 \| 517 \| 515 \| 2494 \|
	\| Millepore \| 746 \| 282 \| 273 \| 1301 \|
	\| No_acropore_encrusting \| 1993 \| 751 \| 728 \| 3472 \|
	\| No_acropore_massive \| 4450 \| 1581 \| 1649 \| 7680 \|
	\| No_acropore_sub_massive \| 3034 \| 1102 \| 1113 \| 5249 \|
	\| Rock \| 10225 \| 3429 \| 3445 \| 17099 \|
	\| Rubble \| 9353 \| 3100 \| 3105 \| 15558 \|
	\| Sand \| 9271 \| 3101 \| 3132 \| 15504 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 88.0
	- Learning Rate: 0.001
	- Train Batch Size: 64
	- Eval Batch Size: 64
	- 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 \| 0.3235681354999542 \| 0.2630072840790843 \| 0.8262109753225342 \| 0.5774239185038708 \| 0.001
	2 \| 0.3146470785140991 \| 0.24115504682622269 \| 0.8378565084377776 \| 0.6199165901601139 \| 0.001
	3 \| 0.3090434670448303 \| 0.2554630593132154 \| 0.8398465111582348 \| 0.6043570009634397 \| 0.001
	4 \| 0.30735355615615845 \| 0.25624349635796045 \| 0.8348980169243037 \| 0.600278483167516 \| 0.001
	5 \| 0.30385810136795044 \| 0.2515608740894901 \| 0.8405948994360434 \| 0.6247746971203368 \| 0.001
	6 \| 0.3059956729412079 \| 0.2596253902185224 \| 0.841987466427932 \| 0.6225111439021958 \| 0.001
	7 \| 0.3013758361339569 \| 0.28199791883454733 \| 0.8387498056289846 \| 0.5954695621655504 \| 0.001
	8 \| 0.30131709575653076 \| 0.2702913631633715 \| 0.8390550208451284 \| 0.5974832028652961 \| 0.001
	9 \| 0.30098479986190796 \| 0.28407908428720086 \| 0.8406665130922214 \| 0.5974259992816957 \| 0.001
	10 \| 0.30072343349456787 \| 0.27107180020811655 \| 0.8376187886791475 \| 0.5937940362628795 \| 0.001
	11 \| 0.3035621643066406 \| 0.277315296566077 \| 0.8348592565387339 \| 0.5761905737205768 \| 0.001
	12 \| 0.3012838363647461 \| 0.26742976066597296 \| 0.838466245156027 \| 0.6114755503631268 \| 0.001
	13 \| 0.29778778553009033 \| 0.2648283038501561 \| 0.8421213122252433 \| 0.6145726431106396 \| 0.001
	14 \| 0.29774588346481323 \| 0.27341311134235174 \| 0.8399742101869762 \| 0.605884177295118 \| 0.001
	15 \| 0.29809942841529846 \| 0.2666493236212279 \| 0.8433503513117323 \| 0.6074624445346274 \| 0.001
	16 \| 0.29744812846183777 \| 0.27471383975026015 \| 0.8394100355835181 \| 0.5932952143692389 \| 0.001
	17 \| 0.2983638644218445 \| 0.2663891779396462 \| 0.8437578624264077 \| 0.6146867059353278 \| 0.001
	18 \| 0.3023049235343933 \| 0.2762747138397503 \| 0.8356339535005088 \| 0.5803903225868541 \| 0.001
	19 \| 0.2984697222709656 \| 0.2739334027055151 \| 0.8423529411764706 \| 0.6158875389283108 \| 0.001
	20 \| 0.29680272936820984 \| 0.28069719042663893 \| 0.8411767731317183 \| 0.5984147849283556 \| 0.001
	21 \| 0.30051520466804504 \| 0.2702913631633715 \| 0.8418969323285377 \| 0.6060492619397649 \| 0.001
	22 \| 0.29818177223205566 \| 0.27471383975026015 \| 0.8374817746302854 \| 0.580353532272699 \| 0.001
	23 \| 0.29393449425697327 \| 0.27809573361082207 \| 0.8436262061960386 \| 0.615237110287355 \| 0.001
	24 \| 0.2948347330093384 \| 0.27601456815816855 \| 0.8453232862164007 \| 0.6228721497006335 \| 0.001
	25 \| 0.29676035046577454 \| 0.2736732570239334 \| 0.8427456149244652 \| 0.610255370235793 \| 0.001
	26 \| 0.2955995500087738 \| 0.2754942767950052 \| 0.8420542140997499 \| 0.6045462014226007 \| 0.001
	27 \| 0.29585039615631104 \| 0.27653485952133194 \| 0.8437684356323902 \| 0.6115221375683754 \| 0.001
	28 \| 0.295540988445282 \| 0.26925078043704476 \| 0.8446938104986479 \| 0.6191186747828321 \| 0.001
	29 \| 0.3010655343532562 \| 0.2663891779396462 \| 0.8437664387164651 \| 0.6215750043898619 \| 0.001
	30 \| 0.29214760661125183 \| 0.2809573361082206 \| 0.8437435686355217 \| 0.6025311078598518 \| 0.0001
	31 \| 0.29040178656578064 \| 0.28121748178980227 \| 0.8439103638567266 \| 0.6071651131848005 \| 0.0001
	32 \| 0.29034462571144104 \| 0.2809573361082206 \| 0.8437194965322373 \| 0.6111569473926136 \| 0.0001
	33 \| 0.2888760268688202 \| 0.28537981269510926 \| 0.8461617038663874 \| 0.6202495870793918 \| 0.0001
	34 \| 0.28964364528656006 \| 0.2861602497398543 \| 0.8446023671361742 \| 0.6150504150317478 \| 0.0001
	35 \| 0.28874215483665466 \| 0.2866805411030177 \| 0.8449244728566273 \| 0.611180048847438 \| 0.0001
	36 \| 0.2888963222503662 \| 0.28355879292403746 \| 0.8447173058645225 \| 0.6119874534823754 \| 0.0001
	37 \| 0.288282573223114 \| 0.2866805411030177 \| 0.8475834540970686 \| 0.6255767175486281 \| 0.0001
	38 \| 0.29050976037979126 \| 0.28251821019771073 \| 0.8452536426724028 \| 0.6057239934398935 \| 0.0001
	39 \| 0.28778275847435 \| 0.28537981269510926 \| 0.8470600182796791 \| 0.625366961909805 \| 0.0001
	40 \| 0.2885717749595642 \| 0.2809573361082206 \| 0.8468000302716884 \| 0.622337777946806 \| 0.0001
	41 \| 0.28773826360702515 \| 0.2843392299687825 \| 0.847323400258903 \| 0.6260539681026288 \| 0.0001
	42 \| 0.28776827454566956 \| 0.28563995837669093 \| 0.8476613005450627 \| 0.6199392946357273 \| 0.0001
	43 \| 0.28717148303985596 \| 0.28303850156087407 \| 0.8479237095716232 \| 0.6287571427217789 \| 0.0001
	44 \| 0.28678667545318604 \| 0.28407908428720086 \| 0.8463665693654939 \| 0.6189979239207937 \| 0.0001
	45 \| 0.28698909282684326 \| 0.28381893860561913 \| 0.8462928555066304 \| 0.6235508782461164 \| 0.0001
	46 \| 0.2868472635746002 \| 0.28251821019771073 \| 0.8459846547314578 \| 0.6151318511304835 \| 0.0001
	47 \| 0.28715068101882935 \| 0.2845993756503642 \| 0.8462129359348595 \| 0.6211457155619424 \| 0.0001
	48 \| 0.28661593794822693 \| 0.28355879292403746 \| 0.8466852933705867 \| 0.6231150403485404 \| 0.0001
	49 \| 0.28633347153663635 \| 0.28590010405827265 \| 0.8460415439387342 \| 0.616055362439494 \| 0.0001
	50 \| 0.28642749786376953 \| 0.2845993756503642 \| 0.8482882700250868 \| 0.625458075101288 \| 0.0001
	51 \| 0.2890762686729431 \| 0.28485952133194586 \| 0.848592785832539 \| 0.6278100779578839 \| 0.0001
	52 \| 0.2855978012084961 \| 0.2851196670135276 \| 0.8464228285561143 \| 0.6255462096645672 \| 0.0001
	53 \| 0.2872205674648285 \| 0.27887617065556713 \| 0.8489991514001897 \| 0.6457587856102145 \| 0.0001
	54 \| 0.2855803072452545 \| 0.2903225806451613 \| 0.8476844874709444 \| 0.6243869856844756 \| 0.0001
	55 \| 0.28568968176841736 \| 0.2845993756503642 \| 0.8475136716266056 \| 0.6339630509281279 \| 0.0001
	56 \| 0.28617897629737854 \| 0.2866805411030177 \| 0.8465597622829039 \| 0.6241465491773776 \| 0.0001
	57 \| 0.2870914936065674 \| 0.2861602497398543 \| 0.845436853426201 \| 0.6249269702519318 \| 0.0001
	58 \| 0.2857914865016937 \| 0.28121748178980227 \| 0.8491941382702348 \| 0.6333866717026029 \| 0.0001
	59 \| 0.28617140650749207 \| 0.2887617065556712 \| 0.8468232576049287 \| 0.6178461796051926 \| 1e-05
	60 \| 0.2846605181694031 \| 0.28537981269510926 \| 0.8485033598045205 \| 0.6275748058546806 \| 1e-05
	61 \| 0.2848633825778961 \| 0.28303850156087407 \| 0.8479865171982329 \| 0.6223888517425455 \| 1e-05
	62 \| 0.28548601269721985 \| 0.2843392299687825 \| 0.8469200122586577 \| 0.6247632003821695 \| 1e-05
	63 \| 0.28493326902389526 \| 0.2827783558792924 \| 0.8488979777323336 \| 0.6274806463168713 \| 1e-05
	64 \| 0.28459736704826355 \| 0.28225806451612906 \| 0.8475187206498287 \| 0.6370787064578803 \| 1e-05
	65 \| 0.2860054671764374 \| 0.2869406867845994 \| 0.8467700785794469 \| 0.6240984315849201 \| 1e-05
	66 \| 0.2847185730934143 \| 0.28407908428720086 \| 0.8481340441736481 \| 0.6346693986906206 \| 1e-05
	67 \| 0.28529325127601624 \| 0.28537981269510926 \| 0.8487528745798691 \| 0.6287121285420982 \| 1e-05
	68 \| 0.2852926254272461 \| 0.2866805411030177 \| 0.8480251642525557 \| 0.6321379394582358 \| 1e-05
	69 \| 0.284834623336792 \| 0.28355879292403746 \| 0.847692190707931 \| 0.6397237492354447 \| 1e-05
	70 \| 0.28527727723121643 \| 0.28225806451612906 \| 0.8492167101827677 \| 0.6381143671040704 \| 1e-05
	71 \| 0.28507113456726074 \| 0.2882414151925078 \| 0.8475971370143149 \| 0.6325489300082728 \| 1.0000000000000002e-06
	72 \| 0.28452861309051514 \| 0.28485952133194586 \| 0.8474255781269963 \| 0.6236352127811986 \| 1.0000000000000002e-06
	73 \| 0.28448227047920227 \| 0.28121748178980227 \| 0.847641772858811 \| 0.6333277250193455 \| 1.0000000000000002e-06
	74 \| 0.28447526693344116 \| 0.2827783558792924 \| 0.8465770953294945 \| 0.6300187593616763 \| 1.0000000000000002e-06
	75 \| 0.2851284146308899 \| 0.28199791883454733 \| 0.8473772748126625 \| 0.6235297745568456 \| 1.0000000000000002e-06
	76 \| 0.2859683036804199 \| 0.2879812695109261 \| 0.847320835674516 \| 0.6186062513830065 \| 1.0000000000000002e-06
	77 \| 0.2858298718929291 \| 0.28563995837669093 \| 0.8459046737621472 \| 0.6172786558676017 \| 1.0000000000000002e-06
	78 \| 0.28438833355903625 \| 0.2843392299687825 \| 0.8480547459130655 \| 0.6325947858436887 \| 1.0000000000000002e-06
	79 \| 0.2870919704437256 \| 0.2874609781477627 \| 0.8472353346431579 \| 0.617917490234713 \| 1.0000000000000002e-06
	80 \| 0.28482332825660706 \| 0.28381893860561913 \| 0.8477330616403465 \| 0.6286567457369128 \| 1.0000000000000002e-06
	81 \| 0.2847617268562317 \| 0.28537981269510926 \| 0.8489678202792957 \| 0.6304525529970205 \| 1.0000000000000002e-06
	82 \| 0.28511229157447815 \| 0.28590010405827265 \| 0.8480416961845967 \| 0.6394217270135759 \| 1.0000000000000002e-06
	83 \| 0.284644216299057 \| 0.28563995837669093 \| 0.8488055562622434 \| 0.6255055774993536 \| 1.0000000000000002e-06
	84 \| 0.2857225835323334 \| 0.2832986472424558 \| 0.848188643119867 \| 0.6457553263622914 \| 1.0000000000000002e-06
	85 \| 0.28550758957862854 \| 0.28121748178980227 \| 0.848818698673405 \| 0.6339586571635658 \| 1.0000000000000002e-07
	86 \| 0.284895658493042 \| 0.28590010405827265 \| 0.8479890588592848 \| 0.6362631688004041 \| 1.0000000000000002e-07
	87 \| 0.2845035493373871 \| 0.2851196670135276 \| 0.8473590201582036 \| 0.6327749126527296 \| 1.0000000000000002e-07
	88 \| 0.28541097044944763 \| 0.28121748178980227 \| 0.8477551536613127 \| 0.6370893160624239 \| 1.0000000000000002e-07


	---

	# Framework Versions

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