DinoVd'eau is a fine-tuned version of facebook/dinov2-large. It achieves the following results on the test set:
- Explained variance: 0.3915
- Loss: 0.3204
- MAE: 0.1180
- MSE: 0.0351
- R2: 0.3909
- RMSE: 0.1874
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.
The source code for training the model can be found in this Git repository.
- Developed by: lombardata, credits to César Leblanc and Victor Illien
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 | val | test | Total |
---|---|---|---|---|
Acropore_branched | 2371 | 782 | 785 | 3938 |
Acropore_digitised | 1693 | 580 | 579 | 2852 |
Acropore_sub_massive | 353 | 99 | 97 | 549 |
Acropore_tabular | 1112 | 420 | 410 | 1942 |
Algae | 13150 | 4386 | 4405 | 21941 |
Dead_coral | 6824 | 2242 | 2250 | 11316 |
Millepore | 1543 | 611 | 631 | 2785 |
No_acropore_encrusting | 2799 | 1044 | 1041 | 4884 |
No_acropore_massive | 6578 | 2216 | 2170 | 10964 |
No_acropore_sub_massive | 5252 | 1802 | 1793 | 8847 |
Rock | 13532 | 4529 | 4529 | 22590 |
Rubble | 12641 | 4222 | 4231 | 21094 |
Sand | 13315 | 4438 | 4438 | 22191 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- Number of Epochs: 100
- 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 | Explained Variance | Validation Loss | MAE | MSE | R2 | RMSE | Learning Rate |
---|---|---|---|---|---|---|---|
1 | 0.265 | 0.348 | 0.135 | 0.043 | 0.26 | 0.207 | 0.001 |
2 | 0.284 | 0.342 | 0.134 | 0.041 | 0.279 | 0.203 | 0.001 |
3 | 0.303 | 0.339 | 0.13 | 0.04 | 0.301 | 0.201 | 0.001 |
4 | 0.287 | 0.344 | 0.14 | 0.042 | 0.267 | 0.205 | 0.001 |
5 | 0.319 | 0.335 | 0.126 | 0.04 | 0.315 | 0.199 | 0.001 |
6 | 0.324 | 0.335 | 0.124 | 0.039 | 0.321 | 0.198 | 0.001 |
7 | 0.319 | 0.335 | 0.129 | 0.039 | 0.315 | 0.198 | 0.001 |
8 | 0.325 | 0.334 | 0.128 | 0.039 | 0.324 | 0.198 | 0.001 |
9 | 0.335 | 0.333 | 0.127 | 0.039 | 0.332 | 0.197 | 0.001 |
10 | 0.33 | 0.334 | 0.13 | 0.039 | 0.327 | 0.197 | 0.001 |
11 | 0.335 | 0.332 | 0.126 | 0.039 | 0.334 | 0.197 | 0.001 |
12 | 0.336 | 0.332 | 0.127 | 0.038 | 0.334 | 0.196 | 0.001 |
13 | 0.332 | 0.333 | 0.127 | 0.039 | 0.327 | 0.198 | 0.001 |
14 | 0.334 | 0.333 | 0.127 | 0.039 | 0.33 | 0.197 | 0.001 |
15 | 0.339 | 0.331 | 0.125 | 0.038 | 0.338 | 0.196 | 0.001 |
16 | 0.34 | 0.332 | 0.129 | 0.038 | 0.339 | 0.196 | 0.001 |
17 | 0.341 | 0.331 | 0.127 | 0.038 | 0.34 | 0.196 | 0.001 |
18 | 0.335 | 0.332 | 0.126 | 0.039 | 0.332 | 0.197 | 0.001 |
19 | 0.331 | 0.334 | 0.125 | 0.04 | 0.322 | 0.199 | 0.001 |
20 | 0.331 | 0.333 | 0.125 | 0.039 | 0.328 | 0.197 | 0.001 |
21 | 0.336 | 0.332 | 0.127 | 0.038 | 0.336 | 0.196 | 0.001 |
22 | 0.339 | 0.331 | 0.125 | 0.038 | 0.337 | 0.196 | 0.001 |
23 | 0.335 | 0.333 | 0.127 | 0.039 | 0.331 | 0.197 | 0.001 |
24 | 0.351 | 0.329 | 0.124 | 0.038 | 0.35 | 0.194 | 0.0001 |
25 | 0.353 | 0.329 | 0.123 | 0.037 | 0.352 | 0.193 | 0.0001 |
26 | 0.356 | 0.327 | 0.123 | 0.037 | 0.355 | 0.193 | 0.0001 |
27 | 0.358 | 0.328 | 0.123 | 0.037 | 0.357 | 0.192 | 0.0001 |
28 | 0.36 | 0.327 | 0.122 | 0.037 | 0.359 | 0.192 | 0.0001 |
29 | 0.357 | 0.328 | 0.123 | 0.037 | 0.356 | 0.193 | 0.0001 |
30 | 0.361 | 0.326 | 0.123 | 0.037 | 0.361 | 0.192 | 0.0001 |
31 | 0.362 | 0.326 | 0.122 | 0.037 | 0.362 | 0.192 | 0.0001 |
32 | 0.362 | 0.326 | 0.122 | 0.037 | 0.362 | 0.191 | 0.0001 |
33 | 0.364 | 0.326 | 0.122 | 0.037 | 0.363 | 0.191 | 0.0001 |
34 | 0.363 | 0.326 | 0.122 | 0.037 | 0.363 | 0.191 | 0.0001 |
35 | 0.363 | 0.326 | 0.123 | 0.037 | 0.363 | 0.191 | 0.0001 |
36 | 0.364 | 0.326 | 0.123 | 0.037 | 0.364 | 0.191 | 0.0001 |
37 | 0.37 | 0.325 | 0.122 | 0.036 | 0.37 | 0.19 | 0.0001 |
38 | 0.362 | 0.326 | 0.123 | 0.037 | 0.362 | 0.191 | 0.0001 |
39 | 0.37 | 0.325 | 0.121 | 0.036 | 0.369 | 0.19 | 0.0001 |
40 | 0.368 | 0.325 | 0.122 | 0.036 | 0.368 | 0.191 | 0.0001 |
41 | 0.369 | 0.325 | 0.121 | 0.036 | 0.369 | 0.19 | 0.0001 |
42 | 0.369 | 0.325 | 0.122 | 0.036 | 0.369 | 0.19 | 0.0001 |
43 | 0.373 | 0.324 | 0.12 | 0.036 | 0.373 | 0.19 | 0.0001 |
44 | 0.374 | 0.324 | 0.121 | 0.036 | 0.374 | 0.19 | 0.0001 |
45 | 0.373 | 0.324 | 0.121 | 0.036 | 0.372 | 0.19 | 0.0001 |
46 | 0.375 | 0.324 | 0.12 | 0.036 | 0.374 | 0.19 | 0.0001 |
47 | 0.376 | 0.323 | 0.119 | 0.036 | 0.375 | 0.189 | 0.0001 |
48 | 0.376 | 0.324 | 0.12 | 0.036 | 0.375 | 0.189 | 0.0001 |
49 | 0.376 | 0.324 | 0.121 | 0.036 | 0.376 | 0.189 | 0.0001 |
50 | 0.375 | 0.324 | 0.12 | 0.036 | 0.374 | 0.19 | 0.0001 |
51 | 0.373 | 0.324 | 0.12 | 0.036 | 0.371 | 0.19 | 0.0001 |
52 | 0.377 | 0.323 | 0.12 | 0.036 | 0.376 | 0.189 | 0.0001 |
53 | 0.376 | 0.324 | 0.121 | 0.036 | 0.375 | 0.189 | 0.0001 |
54 | 0.375 | 0.323 | 0.121 | 0.036 | 0.375 | 0.189 | 0.0001 |
55 | 0.378 | 0.323 | 0.119 | 0.036 | 0.377 | 0.189 | 0.0001 |
56 | 0.377 | 0.323 | 0.119 | 0.036 | 0.376 | 0.189 | 0.0001 |
57 | 0.377 | 0.323 | 0.119 | 0.036 | 0.377 | 0.189 | 0.0001 |
58 | 0.38 | 0.323 | 0.12 | 0.036 | 0.38 | 0.189 | 0.0001 |
59 | 0.375 | 0.323 | 0.119 | 0.036 | 0.375 | 0.189 | 0.0001 |
60 | 0.379 | 0.323 | 0.119 | 0.036 | 0.378 | 0.189 | 0.0001 |
61 | 0.377 | 0.323 | 0.12 | 0.036 | 0.376 | 0.189 | 0.0001 |
62 | 0.379 | 0.323 | 0.119 | 0.036 | 0.378 | 0.189 | 0.0001 |
63 | 0.377 | 0.323 | 0.12 | 0.036 | 0.377 | 0.189 | 0.0001 |
64 | 0.379 | 0.323 | 0.119 | 0.036 | 0.378 | 0.189 | 0.0001 |
65 | 0.38 | 0.322 | 0.12 | 0.036 | 0.379 | 0.188 | 1e-05 |
66 | 0.381 | 0.322 | 0.119 | 0.035 | 0.381 | 0.188 | 1e-05 |
67 | 0.381 | 0.322 | 0.118 | 0.035 | 0.381 | 0.188 | 1e-05 |
68 | 0.382 | 0.322 | 0.119 | 0.035 | 0.382 | 0.188 | 1e-05 |
69 | 0.382 | 0.322 | 0.119 | 0.035 | 0.382 | 0.188 | 1e-05 |
70 | 0.382 | 0.322 | 0.119 | 0.035 | 0.382 | 0.188 | 1e-05 |
71 | 0.381 | 0.322 | 0.119 | 0.035 | 0.381 | 0.188 | 1e-05 |
72 | 0.383 | 0.322 | 0.118 | 0.035 | 0.382 | 0.188 | 1e-05 |
73 | 0.382 | 0.322 | 0.118 | 0.035 | 0.382 | 0.188 | 1e-05 |
74 | 0.382 | 0.322 | 0.119 | 0.035 | 0.381 | 0.188 | 1e-05 |
75 | 0.382 | 0.322 | 0.119 | 0.035 | 0.381 | 0.188 | 1e-05 |
76 | 0.381 | 0.322 | 0.119 | 0.035 | 0.381 | 0.188 | 1e-05 |
77 | 0.381 | 0.322 | 0.117 | 0.035 | 0.381 | 0.188 | 1.0000000000000002e-06 |
78 | 0.382 | 0.322 | 0.118 | 0.035 | 0.381 | 0.188 | 1.0000000000000002e-06 |
79 | 0.381 | 0.322 | 0.119 | 0.035 | 0.381 | 0.188 | 1.0000000000000002e-06 |
80 | 0.382 | 0.322 | 0.118 | 0.035 | 0.382 | 0.188 | 1.0000000000000002e-06 |
81 | 0.382 | 0.322 | 0.118 | 0.035 | 0.382 | 0.188 | 1.0000000000000002e-06 |
82 | 0.383 | 0.322 | 0.119 | 0.035 | 0.382 | 0.188 | 1.0000000000000002e-06 |
CO2 Emissions
The estimated CO2 emissions for training this model are documented below:
- Emissions: 0.25264438000117284 grams of CO2
- Source: Code Carbon
- Training Type: fine-tuning
- Geographical Location: Brest, France
- Hardware Used: NVIDIA Tesla V100 PCIe 32 Go
Framework Versions
- Transformers: 4.41.1
- Pytorch: 2.3.0+cu121
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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Model tree for lombardata/drone-DinoVdeau-large-2024_08_02-batch-size64_epochs100_freeze
Base model
facebook/dinov2-large