Hugging Face's logo

lombardata
/
bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs

TensorBoard
Safetensors
English
dinov2
multilabel-image-classification
multilabel
Generated from Trainer
Model card Files Metrics Community
bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs / README.md
lombardata's picture
lombardata
Upload README.md
38e9099 verified
preview code
|
raw
history blame
7.56 kB
metadata
language:
  - eng
license: cc0-1.0
tags:
  - multilabel-image-classification
  - multilabel
  - generated_from_trainer
base_model: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
model-index:
  - name: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
    results: []

bd_ortho-DinoVdeau is a fine-tuned version of bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs. It achieves the following results on the test set:

  • Loss: 0.4551
  • RMSE: 0.0866
  • MAE: 0.0630
  • KL Divergence: 0.1147

Model description

bd_ortho-DinoVdeau is a model built on top of bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_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 8074 2667 2658 13399
Acropore_digitised 3730 829 823 5382
Acropore_tabular 125 23 40 188
Algae 14027 4662 4660 23349
Dead_coral 11369 3364 3369 18102
Millepore 2 1 1 4
No_acropore_encrusting 0 0 0 0
No_acropore_massive 3265 423 463 4151
No_acropore_sub_massive 10241 2911 2924 16076
Rock 14090 4694 4693 23477
Rubble 12455 3915 3883 20253
Sand 12848 4098 4079 21025

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • Number of Epochs: 62.0
  • 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 MAE RMSE KL div Learning Rate
1 0.46336060762405396 0.0760 0.1018 0.0696 0.001
2 0.45933997631073 0.0716 0.0952 0.0038 0.001
3 0.457367479801178 0.0670 0.0918 0.0583 0.001
4 0.459468811750412 0.0713 0.0955 -0.0650 0.001
5 0.45927393436431885 0.0702 0.0954 -0.0835 0.001
6 0.46080395579338074 0.0728 0.0977 -0.0705 0.001
7 0.4581476151943207 0.0683 0.0927 -0.0044 0.001
8 0.4573117196559906 0.0680 0.0916 0.0799 0.001
9 0.45939013361930847 0.0706 0.0947 0.0233 0.001
10 0.45772281289100647 0.0675 0.0918 0.0885 0.001
11 0.45641985535621643 0.0662 0.0898 0.1296 0.001
12 0.45718902349472046 0.0677 0.0913 -0.0061 0.001
13 0.4622880220413208 0.0747 0.1002 -0.2060 0.001
14 0.45775285363197327 0.0678 0.0925 -0.0371 0.001
15 0.4575214684009552 0.0667 0.0917 0.0458 0.001
16 0.4578736424446106 0.0680 0.0926 0.0151 0.001
17 0.4592094421386719 0.0702 0.0949 -0.0679 0.001
18 0.45573291182518005 0.0651 0.0887 0.0421 0.0001
19 0.4555513262748718 0.0647 0.0885 0.0468 0.0001
20 0.45553284883499146 0.0648 0.0884 0.0405 0.0001
21 0.4555487334728241 0.0650 0.0884 0.0475 0.0001
22 0.45551028847694397 0.0646 0.0883 0.0570 0.0001
23 0.45505577325820923 0.0641 0.0874 0.0887 0.0001
24 0.4552234709262848 0.0642 0.0878 0.0555 0.0001
25 0.45521080493927 0.0645 0.0878 0.0238 0.0001
26 0.4557025730609894 0.0646 0.0885 0.0409 0.0001
27 0.4550967216491699 0.0639 0.0876 0.0548 0.0001
28 0.45512688159942627 0.0642 0.0876 0.0273 0.0001
29 0.45477041602134705 0.0634 0.0869 0.0744 0.0001
30 0.4549327790737152 0.0636 0.0873 0.0492 0.0001
31 0.4547973871231079 0.0632 0.0869 0.0688 0.0001
32 0.454988956451416 0.0639 0.0874 0.0271 0.0001
33 0.455375999212265 0.0647 0.0882 -0.0174 0.0001
34 0.45461305975914 0.0628 0.0866 0.1094 0.0001
35 0.45498156547546387 0.0639 0.0874 0.0571 0.0001
36 0.4548388123512268 0.0629 0.0869 0.1453 0.0001
37 0.45526784658432007 0.0645 0.0881 -0.0152 0.0001
38 0.45479556918144226 0.0636 0.0870 0.0490 0.0001
39 0.454780250787735 0.0631 0.0870 0.0726 0.0001
40 0.45476558804512024 0.0632 0.0870 0.0637 0.0001
41 0.45470812916755676 0.0634 0.0869 0.0390 1e-05
42 0.4543863534927368 0.0628 0.0862 0.1115 1e-05
43 0.4545557498931885 0.0632 0.0866 0.0533 1e-05
44 0.45448434352874756 0.0625 0.0864 0.1350 1e-05
45 0.4550137519836426 0.0642 0.0874 0.0044 1e-05
46 0.4545902609825134 0.0632 0.0867 0.0389 1e-05
47 0.4544997215270996 0.0630 0.0866 0.0370 1e-05
48 0.4546374976634979 0.0634 0.0868 0.0194 1e-05
49 0.45436596870422363 0.0627 0.0862 0.0667 1.0000000000000002e-06
50 0.45450592041015625 0.0631 0.0865 0.0548 1.0000000000000002e-06
51 0.4544804096221924 0.0629 0.0865 0.0428 1.0000000000000002e-06
52 0.45421910285949707 0.0623 0.0859 0.1236 1.0000000000000002e-06
53 0.4542272686958313 0.0625 0.0859 0.0887 1.0000000000000002e-06
54 0.4543103575706482 0.0624 0.0862 0.0917 1.0000000000000002e-06
55 0.45456644892692566 0.0631 0.0865 0.0774 1.0000000000000002e-06
56 0.45458319783210754 0.0633 0.0866 0.0473 1.0000000000000002e-06
57 0.4548773169517517 0.0639 0.0871 -0.0046 1.0000000000000002e-06
58 0.45440155267715454 0.0627 0.0864 0.0553 1.0000000000000002e-06
59 0.45448538661003113 0.0631 0.0865 0.0368 1.0000000000000002e-07
60 0.4544091522693634 0.0629 0.0863 0.0471 1.0000000000000002e-07
61 0.4542348086833954 0.0624 0.0860 0.0928 1.0000000000000002e-07
62 0.4545469284057617 0.0632 0.0866 0.0286 1.0000000000000002e-07

Framework Versions

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