File size: 8,440 Bytes
439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe 2e3f7df 439b1fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
---
language:
- eng
license: cc0-1.0
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs
model-index:
- name: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs
results: []
---
drone-DinoVdeau-from-probs is a fine-tuned version of [drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs). It achieves the following results on the test set:
- Loss: 0.4672
- RMSE: 0.1553
- MAE: 0.1147
- KL Divergence: 0.3577
---
# Model description
drone-DinoVdeau-from-probs is a model built on top of drone-DinoVdeau-from-probs-large-2024_11_15-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](https://github.com/SeatizenDOI/DinoVdeau).
- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)
---
# 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**: 79.0
- **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 | 0.5005590319633484 | 0.1552 | 0.1904 | 0.1025 | 0.001
2 | 0.47547808289527893 | 0.1245 | 0.1681 | 0.5180 | 0.001
3 | 0.47452571988105774 | 0.1227 | 0.1675 | 0.6862 | 0.001
4 | 0.47420722246170044 | 0.1255 | 0.1672 | 0.3212 | 0.001
5 | 0.47245556116104126 | 0.1224 | 0.1653 | 0.5072 | 0.001
6 | 0.4725925624370575 | 0.1216 | 0.1657 | 0.6710 | 0.001
7 | 0.4731809198856354 | 0.1255 | 0.1655 | 0.3162 | 0.001
8 | 0.47284314036369324 | 0.1260 | 0.1651 | 0.2719 | 0.001
9 | 0.4707973003387451 | 0.1206 | 0.1639 | 0.6393 | 0.001
10 | 0.4732784628868103 | 0.1230 | 0.1654 | 0.5359 | 0.001
11 | 0.47162503004074097 | 0.1253 | 0.1647 | 0.2479 | 0.001
12 | 0.47083696722984314 | 0.1244 | 0.1631 | 0.3119 | 0.001
13 | 0.47152063250541687 | 0.1230 | 0.1635 | 0.3694 | 0.001
14 | 0.47212228178977966 | 0.1216 | 0.1653 | 0.5592 | 0.001
15 | 0.47012239694595337 | 0.1213 | 0.1628 | 0.4936 | 0.001
16 | 0.4718552827835083 | 0.1229 | 0.1646 | 0.2820 | 0.001
17 | 0.46933484077453613 | 0.1200 | 0.1621 | 0.5294 | 0.001
18 | 0.4710436165332794 | 0.1216 | 0.1635 | 0.4093 | 0.001
19 | 0.4698491394519806 | 0.1219 | 0.1622 | 0.2918 | 0.001
20 | 0.4691685736179352 | 0.1190 | 0.1617 | 0.4772 | 0.001
21 | 0.46830564737319946 | 0.1204 | 0.1606 | 0.4336 | 0.001
22 | 0.47239789366722107 | 0.1183 | 0.1650 | 0.7962 | 0.001
23 | 0.47136834263801575 | 0.1223 | 0.1641 | 0.2854 | 0.001
24 | 0.4706868529319763 | 0.1207 | 0.1633 | 0.4206 | 0.001
25 | 0.46786901354789734 | 0.1185 | 0.1606 | 0.5436 | 0.001
26 | 0.47084224224090576 | 0.1192 | 0.1634 | 0.4964 | 0.001
27 | 0.4695045053958893 | 0.1185 | 0.1625 | 0.6399 | 0.001
28 | 0.4700873792171478 | 0.1184 | 0.1624 | 0.5737 | 0.001
29 | 0.4698559045791626 | 0.1200 | 0.1624 | 0.4459 | 0.001
30 | 0.4722815454006195 | 0.1254 | 0.1643 | 0.2726 | 0.001
31 | 0.46958214044570923 | 0.1184 | 0.1622 | 0.5308 | 0.001
32 | 0.46677276492118835 | 0.1175 | 0.1593 | 0.4200 | 0.0001
33 | 0.46626824140548706 | 0.1177 | 0.1587 | 0.3529 | 0.0001
34 | 0.46665358543395996 | 0.1181 | 0.1592 | 0.3588 | 0.0001
35 | 0.46587392687797546 | 0.1160 | 0.1584 | 0.4813 | 0.0001
36 | 0.46578526496887207 | 0.1173 | 0.1581 | 0.3504 | 0.0001
37 | 0.4654408395290375 | 0.1158 | 0.1578 | 0.3919 | 0.0001
38 | 0.46546319127082825 | 0.1166 | 0.1580 | 0.4058 | 0.0001
39 | 0.465843141078949 | 0.1174 | 0.1585 | 0.4118 | 0.0001
40 | 0.46561121940612793 | 0.1170 | 0.1579 | 0.3564 | 0.0001
41 | 0.4657152593135834 | 0.1171 | 0.1582 | 0.3573 | 0.0001
42 | 0.4651602804660797 | 0.1155 | 0.1579 | 0.5042 | 0.0001
43 | 0.4651065468788147 | 0.1157 | 0.1575 | 0.4462 | 0.0001
44 | 0.46537330746650696 | 0.1166 | 0.1579 | 0.4236 | 0.0001
45 | 0.46489208936691284 | 0.1151 | 0.1574 | 0.4510 | 0.0001
46 | 0.46484702825546265 | 0.1157 | 0.1575 | 0.4490 | 0.0001
47 | 0.4648602306842804 | 0.1152 | 0.1574 | 0.4751 | 0.0001
48 | 0.4647873342037201 | 0.1151 | 0.1575 | 0.5305 | 0.0001
49 | 0.4647849500179291 | 0.1154 | 0.1574 | 0.4799 | 0.0001
50 | N/A | 0.0000 | 0.0000 | 0.0000 | 0.0001
51 | 0.465638667345047 | 0.1151 | 0.1582 | 0.4879 | 0.0001
52 | 0.46429532766342163 | 0.1155 | 0.1566 | 0.4199 | 0.0001
53 | 0.46441230177879333 | 0.1156 | 0.1569 | 0.3880 | 0.0001
54 | 0.4646008610725403 | 0.1148 | 0.1569 | 0.4229 | 0.0001
55 | 0.4644174873828888 | 0.1159 | 0.1569 | 0.4009 | 0.0001
56 | 0.464743047952652 | 0.1164 | 0.1572 | 0.3405 | 0.0001
57 | 0.4645179808139801 | 0.1152 | 0.1569 | 0.4188 | 0.0001
58 | 0.465102881193161 | 0.1164 | 0.1576 | 0.3079 | 0.0001
59 | 0.4644688367843628 | 0.1150 | 0.1570 | 0.4339 | 1e-05
60 | 0.46417686343193054 | 0.1150 | 0.1566 | 0.3894 | 1e-05
61 | 0.4639436900615692 | 0.1146 | 0.1563 | 0.4145 | 1e-05
62 | 0.4641311764717102 | 0.1148 | 0.1565 | 0.4064 | 1e-05
63 | 0.4643491506576538 | 0.1149 | 0.1565 | 0.3542 | 1e-05
64 | 0.46402981877326965 | 0.1150 | 0.1564 | 0.3718 | 1e-05
65 | 0.4640822410583496 | 0.1152 | 0.1565 | 0.4128 | 1e-05
66 | 0.46441909670829773 | 0.1145 | 0.1570 | 0.4988 | 1e-05
67 | 0.46383005380630493 | 0.1151 | 0.1562 | 0.4122 | 1e-05
68 | 0.4639807641506195 | 0.1144 | 0.1565 | 0.4579 | 1e-05
69 | 0.4637599587440491 | 0.1143 | 0.1561 | 0.4197 | 1e-05
70 | 0.46392253041267395 | 0.1145 | 0.1563 | 0.4286 | 1e-05
71 | 0.46406444907188416 | 0.1153 | 0.1563 | 0.3542 | 1e-05
72 | 0.46417826414108276 | 0.1147 | 0.1566 | 0.4250 | 1e-05
73 | 0.4637835919857025 | 0.1140 | 0.1561 | 0.4397 | 1e-05
74 | 0.463798850774765 | 0.1145 | 0.1563 | 0.4437 | 1e-05
75 | 0.46379053592681885 | 0.1145 | 0.1561 | 0.4049 | 1e-05
76 | 0.4639701247215271 | 0.1141 | 0.1565 | 0.4926 | 1.0000000000000002e-06
77 | 0.463869571685791 | 0.1142 | 0.1562 | 0.4427 | 1.0000000000000002e-06
78 | 0.46388140320777893 | 0.1145 | 0.1563 | 0.4293 | 1.0000000000000002e-06
79 | 0.46412238478660583 | 0.1147 | 0.1564 | 0.3765 | 1.0000000000000002e-06
---
# Framework Versions
- **Transformers**: 4.41.0
- **Pytorch**: 2.5.0+cu124
- **Datasets**: 3.0.2
- **Tokenizers**: 0.19.1
|