dinov2-large-linearhead-2024_03_06-with_data_aug_batch-size32_epochs93_freeze
DinoVd'eau is a fine-tuned version of facebook/dinov2-large on the multilabel_complete_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.0947
- F1 Micro: 0.8528
- F1 Macro: 0.8273
- Roc Auc: 0.9054
- Accuracy: 0.5468
- Learning Rate: 0.0000
Model description
DinoVd'eau is a model built on top of dinov2 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.
- Developed by: lombardata, credits to César Leblanc
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:
train | val | test | Total | |
---|---|---|---|---|
Acropore_branched | 804 | 202 | 200 | 1206 |
Acropore_digitised | 465 | 108 | 101 | 674 |
Acropore_tabular | 964 | 276 | 267 | 1507 |
Algae_assembly | 2172 | 692 | 698 | 3562 |
Algae_limestone | 1327 | 439 | 441 | 2207 |
Algae_sodding | 2079 | 676 | 671 | 3426 |
Dead_coral | 1126 | 358 | 355 | 1839 |
Fish | 874 | 243 | 242 | 1359 |
Human_object | 407 | 135 | 136 | 678 |
Living_coral | 1765 | 580 | 571 | 2916 |
Millepore | 350 | 119 | 102 | 571 |
No_acropore_encrusting | 411 | 142 | 129 | 682 |
No_acropore_foliaceous | 212 | 34 | 39 | 285 |
No_acropore_massive | 921 | 317 | 310 | 1548 |
No_acropore_sub_massive | 1205 | 362 | 363 | 1930 |
Rock | 3736 | 1218 | 1217 | 6171 |
Sand | 3594 | 1202 | 1194 | 5990 |
Scrap | 2121 | 724 | 741 | 3586 |
Sea_cucumber | 781 | 254 | 265 | 1300 |
Sea_urchins | 189 | 60 | 72 | 321 |
Sponge | 226 | 75 | 88 | 389 |
Syringodium_isoetifolium | 1171 | 386 | 392 | 1949 |
Thalassodendron_ciliatum | 783 | 261 | 260 | 1304 |
Useless | 587 | 195 | 195 | 977 |
Training procedure
Data Augmentation
Data were augmented using the following transformations :
- training transformations : Sequential( (0): PreProcess() (1): Resize(output_size=(518, 518), p=1.0, p_batch=1.0, same_on_batch=True, size=(518, 518), side=short, resample=bilinear, align_corners=True, antialias=False) (2): RandomHorizontalFlip(p=0.25, p_batch=1.0, same_on_batch=False) (3): RandomVerticalFlip(p=0.25, p_batch=1.0, same_on_batch=False) (4): ColorJiggle(brightness=0.0, contrast=0.0, saturation=0.0, hue=0.0, p=0.25, p_batch=1.0, same_on_batch=False) (5): RandomPerspective(distortion_scale=0.5, p=0.25, p_batch=1.0, same_on_batch=False, align_corners=False, resample=bilinear) (6): Normalize(p=1.0, p_batch=1.0, same_on_batch=True, mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250])) )
- validation transformations : Sequential( (0): PreProcess() (1): Resize(output_size=(518, 518), p=1.0, p_batch=1.0, same_on_batch=True, size=(518, 518), side=short, resample=bilinear, align_corners=True, antialias=False) (2): Normalize(p=1.0, p_batch=1.0, same_on_batch=True, mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250])) )
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- freeze_encoder: True
- num_epochs: 93
Training results
Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 274 | 0.1280 | 0.7998 | 0.7183 | 0.8765 | 0.4798 | 0.001 |
0.1493 | 2.0 | 548 | 0.1210 | 0.8095 | 0.7528 | 0.8793 | 0.5080 | 0.001 |
0.1493 | 3.0 | 822 | 0.1156 | 0.8119 | 0.7609 | 0.8667 | 0.5237 | 0.001 |
0.1156 | 4.0 | 1096 | 0.1167 | 0.8240 | 0.7881 | 0.9000 | 0.5003 | 0.001 |
0.1156 | 5.0 | 1370 | 0.1073 | 0.8367 | 0.7939 | 0.8968 | 0.5254 | 0.001 |
0.1069 | 6.0 | 1644 | 0.1082 | 0.8319 | 0.7918 | 0.8911 | 0.5341 | 0.001 |
0.1069 | 7.0 | 1918 | 0.1065 | 0.8367 | 0.7811 | 0.8963 | 0.5390 | 0.001 |
0.1026 | 8.0 | 2192 | 0.1071 | 0.8364 | 0.8001 | 0.8959 | 0.5351 | 0.001 |
0.1026 | 9.0 | 2466 | 0.1101 | 0.8346 | 0.7925 | 0.9113 | 0.5111 | 0.001 |
0.1 | 10.0 | 2740 | 0.1074 | 0.8345 | 0.7808 | 0.8973 | 0.5320 | 0.001 |
0.0964 | 11.0 | 3014 | 0.1079 | 0.8375 | 0.7967 | 0.8985 | 0.5292 | 0.001 |
0.0964 | 12.0 | 3288 | 0.1070 | 0.8353 | 0.7908 | 0.8951 | 0.5341 | 0.001 |
0.0949 | 13.0 | 3562 | 0.1060 | 0.8371 | 0.7926 | 0.8987 | 0.5296 | 0.001 |
0.0949 | 14.0 | 3836 | 0.1035 | 0.8443 | 0.7987 | 0.9007 | 0.5438 | 0.001 |
0.0926 | 15.0 | 4110 | 0.1099 | 0.8363 | 0.8004 | 0.9060 | 0.5118 | 0.001 |
0.0926 | 16.0 | 4384 | 0.1086 | 0.8327 | 0.7991 | 0.8886 | 0.5355 | 0.001 |
0.0911 | 17.0 | 4658 | 0.1084 | 0.8333 | 0.7967 | 0.8952 | 0.5209 | 0.001 |
0.0911 | 18.0 | 4932 | 0.1083 | 0.8358 | 0.7976 | 0.8968 | 0.5344 | 0.001 |
0.0902 | 19.0 | 5206 | 0.1129 | 0.8301 | 0.7799 | 0.8829 | 0.5233 | 0.001 |
0.0902 | 20.0 | 5480 | 0.1033 | 0.8464 | 0.8107 | 0.9065 | 0.5400 | 0.001 |
0.0896 | 21.0 | 5754 | 0.1091 | 0.8375 | 0.8014 | 0.9018 | 0.5233 | 0.001 |
0.0881 | 22.0 | 6028 | 0.1040 | 0.8412 | 0.7987 | 0.8995 | 0.5383 | 0.001 |
0.0881 | 23.0 | 6302 | 0.1090 | 0.8385 | 0.7908 | 0.9012 | 0.5278 | 0.001 |
0.0874 | 24.0 | 6576 | 0.1078 | 0.8338 | 0.7961 | 0.8917 | 0.5313 | 0.001 |
0.0874 | 25.0 | 6850 | 0.1054 | 0.8455 | 0.8077 | 0.9023 | 0.5501 | 0.001 |
0.0864 | 26.0 | 7124 | 0.1085 | 0.8346 | 0.7913 | 0.8860 | 0.5348 | 0.001 |
0.0864 | 27.0 | 7398 | 0.0994 | 0.8486 | 0.8134 | 0.9040 | 0.5487 | 0.0001 |
0.0793 | 28.0 | 7672 | 0.0989 | 0.8495 | 0.8123 | 0.9039 | 0.5532 | 0.0001 |
0.0793 | 29.0 | 7946 | 0.0986 | 0.8485 | 0.8107 | 0.9028 | 0.5511 | 0.0001 |
0.0751 | 30.0 | 8220 | 0.0986 | 0.8510 | 0.8188 | 0.9080 | 0.5501 | 0.0001 |
0.0751 | 31.0 | 8494 | 0.0990 | 0.8488 | 0.8139 | 0.9034 | 0.5539 | 0.0001 |
0.0753 | 32.0 | 8768 | 0.0983 | 0.8510 | 0.8181 | 0.9048 | 0.5505 | 0.0001 |
0.0748 | 33.0 | 9042 | 0.0987 | 0.8494 | 0.8110 | 0.9018 | 0.5539 | 0.0001 |
0.0748 | 34.0 | 9316 | 0.0980 | 0.8501 | 0.8117 | 0.9045 | 0.5515 | 0.0001 |
0.0748 | 35.0 | 9590 | 0.0981 | 0.8502 | 0.8133 | 0.9064 | 0.5505 | 0.0001 |
0.0748 | 36.0 | 9864 | 0.0984 | 0.8507 | 0.8130 | 0.9045 | 0.5536 | 0.0001 |
0.0745 | 37.0 | 10138 | 0.0983 | 0.8507 | 0.8145 | 0.9067 | 0.5484 | 0.0001 |
0.0745 | 38.0 | 10412 | 0.0986 | 0.8486 | 0.8107 | 0.9011 | 0.5546 | 0.0001 |
0.0749 | 39.0 | 10686 | 0.0986 | 0.8491 | 0.8140 | 0.9029 | 0.5508 | 0.0001 |
0.0749 | 40.0 | 10960 | 0.0982 | 0.8487 | 0.8114 | 0.9002 | 0.5553 | 0.0001 |
0.0752 | 41.0 | 11234 | 0.0976 | 0.8505 | 0.8131 | 0.9058 | 0.5508 | 1e-05 |
0.0734 | 42.0 | 11508 | 0.0977 | 0.8500 | 0.8128 | 0.9046 | 0.5515 | 1e-05 |
0.0734 | 43.0 | 11782 | 0.0975 | 0.8498 | 0.8118 | 0.9053 | 0.5515 | 1e-05 |
0.0736 | 44.0 | 12056 | 0.0976 | 0.8495 | 0.8118 | 0.9046 | 0.5522 | 1e-05 |
0.0736 | 45.0 | 12330 | 0.0975 | 0.8503 | 0.8119 | 0.9053 | 0.5508 | 1e-05 |
0.0731 | 46.0 | 12604 | 0.0976 | 0.8498 | 0.8119 | 0.9046 | 0.5511 | 1e-05 |
0.0731 | 47.0 | 12878 | 0.0975 | 0.8500 | 0.8115 | 0.9046 | 0.5518 | 1e-05 |
0.0736 | 48.0 | 13152 | 0.0975 | 0.8505 | 0.8141 | 0.9052 | 0.5511 | 1e-05 |
0.0736 | 49.0 | 13426 | 0.0975 | 0.8504 | 0.8144 | 0.9053 | 0.5518 | 1e-05 |
0.073 | 50.0 | 13700 | 0.0975 | 0.8502 | 0.8138 | 0.9052 | 0.5518 | 0.0000 |
0.073 | 51.0 | 13974 | 0.0975 | 0.8499 | 0.8123 | 0.9049 | 0.5515 | 0.0000 |
0.0732 | 52.0 | 14248 | 0.0975 | 0.8500 | 0.8119 | 0.9049 | 0.5515 | 0.0000 |
0.0732 | 53.0 | 14522 | 0.0975 | 0.8499 | 0.8118 | 0.9047 | 0.5522 | 0.0000 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.15.0
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Model tree for lombardata/dinov2-large-linearhead-2024_03_06-with_data_aug_batch-size32_epochs93_freeze
Base model
facebook/dinov2-large