Resneteau-50-2024_09_23-batch-size32_freeze

Resneteau is a fine-tuned version of microsoft/resnet-50. It achieves the following results on the test set:

• Loss: 0.1906
• F1 Micro: 0.6954
• F1 Macro: 0.4462
• Accuracy: 0.1827

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Model description

Resneteau is a model built on top of microsoft/resnet-50 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

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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.

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Training and evaluation data

Details on the number of images for each class are given in the following table:

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• Number of Epochs: 28.0
• Learning Rate: 0.001
• Train Batch Size: 32
• Eval Batch Size: 32
• 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

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CO2 Emissions

The estimated CO2 emissions for training this model are documented below:

• Emissions: 0.1871415951855612 grams of CO2
• Source: Code Carbon
• Training Type: fine-tuning
• Geographical Location: Brest, France
• Hardware Used: NVIDIA Tesla V100 PCIe 32 Go

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Framework Versions

• Transformers: 4.44.2
• Pytorch: 2.4.1+cu121
• Datasets: 3.0.0
• Tokenizers: 0.19.1