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drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs

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dinov2
multilabel-image-classification
multilabel
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metadata
language:
  - eng
license: cc0-1.0
tags:
  - multilabel-image-classification
  - multilabel
  - generated_from_trainer
base_model: >-
  drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs
model-index:
  - name: >-
      drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs
    results: []

drone-DinoVdeau-produttoria-probabilities is a fine-tuned version of drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-batch-size16_freeze_probs. It achieves the following results on the test set:

  • Loss: 0.3261
  • F1 Micro: 0.8621
  • F1 Macro: 0.8264
  • Accuracy: 0.1682
  • RMSE: 0.2445
  • MAE: 0.1621
  • R2: 0.4057
Class F1 per class
Acropore_branched 0.8063
Acropore_digitised 0.7335
Acropore_tabular 0.6247
Algae 0.9859
Dead_coral 0.8424
Fish 0.7464
Millepore 0.6453
No_acropore_encrusting 0.7292
No_acropore_massive 0.8681
No_acropore_sub_massive 0.8092
Rock 0.9925
Rubble 0.9693
Sand 0.9904

Model description

drone-DinoVdeau-produttoria-probabilities is a model built on top of drone-DinoVdeau-produttoria-probabilities-large-2024_11_06-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 2028 684 686 3398
Acropore_digitised 2006 735 717 3458
Acropore_tabular 1237 461 451 2149
Algae 11086 3671 3675 18432
Dead_coral 6354 2161 2147 10662
Fish 4032 1430 1430 6892
Millepore 1943 783 772 3498
No_acropore_encrusting 2663 986 957 4606
No_acropore_massive 6897 2375 2375 11647
No_acropore_sub_massive 5416 1988 1958 9362
Rock 11164 3726 3725 18615
Rubble 10687 3570 3572 17829
Sand 11151 3726 3723 18600

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • Number of Epochs: 45.0
  • 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 R2 Learning Rate
0 N/A 0.0000 0.0000 0.0000 0.001
1 0.36246591806411743 0.1880 0.2669 0.2744 0.001
2 0.3457428216934204 0.1685 0.2560 0.3367 0.001
3 0.3518487811088562 0.1747 0.2597 0.3157 0.001
4 0.3507988750934601 0.1751 0.2563 0.3345 0.001
5 0.3436409533023834 0.1696 0.2546 0.3371 0.001
6 0.35096481442451477 0.1767 0.2598 0.3175 0.001
7 0.3412320613861084 0.1750 0.2538 0.3471 0.001
8 0.3456409275531769 0.1678 0.2561 0.3435 0.001
9 0.3425351679325104 0.1741 0.2545 0.3409 0.001
10 0.33964109420776367 0.1711 0.2525 0.3583 0.001
11 0.34479108452796936 0.1721 0.2542 0.3498 0.001
12 0.3415849804878235 0.1767 0.2527 0.3577 0.001
13 0.33990854024887085 0.1677 0.2527 0.3523 0.001
14 0.34520208835601807 0.1746 0.2540 0.3443 0.001
15 0.34849879145622253 0.1801 0.2568 0.3333 0.001
16 0.34347954392433167 0.1718 0.2537 0.3473 0.001
17 0.341246634721756 0.1711 0.2508 0.3633 0.0001
18 0.3398562967777252 0.1708 0.2507 0.3649 0.0001
19 0.3332718312740326 0.1675 0.2483 0.3775 0.0001
20 0.333162784576416 0.1688 0.2478 0.3810 0.0001
21 0.3324449062347412 0.1673 0.2476 0.3810 0.0001
22 0.3320053517818451 0.1671 0.2472 0.3836 0.0001
23 0.3301050662994385 0.1658 0.2461 0.3890 0.0001
24 0.3298528492450714 0.1648 0.2458 0.3899 0.0001
25 0.32962867617607117 0.1641 0.2458 0.3903 0.0001
26 0.32889437675476074 0.1632 0.2454 0.3926 0.0001
27 0.33042922616004944 0.1674 0.2461 0.3891 0.0001
28 0.32880541682243347 0.1645 0.2451 0.3955 0.0001
29 0.3293789327144623 0.1656 0.2451 0.3961 0.0001
30 0.33135533332824707 0.1684 0.2464 0.3914 0.0001
31 0.32911789417266846 0.1608 0.2457 0.3904 0.0001
32 0.3289436399936676 0.1631 0.2453 0.3959 0.0001
33 0.3271527588367462 0.1628 0.2444 0.3972 0.0001
34 0.32699429988861084 0.1621 0.2443 0.3976 0.0001
35 0.32638314366340637 0.1615 0.2439 0.3987 0.0001
36 0.3293066918849945 0.1656 0.2455 0.3946 0.0001
37 0.3271186649799347 0.1597 0.2442 0.3996 0.0001
38 0.32695677876472473 0.1613 0.2437 0.4022 0.0001
39 0.33263665437698364 0.1575 0.2438 0.4007 0.0001
40 0.33278176188468933 0.1651 0.2442 0.4003 0.0001
41 0.33069443702697754 0.1627 0.2435 0.4031 0.0001
42 0.3310275375843048 0.1641 0.2436 0.4030 1e-05
43 0.32956016063690186 0.1603 0.2429 0.4052 1e-05
44 0.33022987842559814 0.1625 0.2432 0.4038 1e-05
45 0.3266430199146271 0.1617 0.2430 0.4047 1e-05

Framework Versions

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