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drone-DinoVdeau-from-probs-large-2024_11_14-batch-size16_freeze_probs

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metadata
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. 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.

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