Weighted Random Sampler for Multi-class detection

[bw4sz]

The vast majority of ecological data are imbalanced. We should have some default weighted random sampler options

from torch.utils.data import WeightedRandomSampler
from deepforest import main

m = main.deepforest()

[setup and load data]

dataset = m.train_ds

# Assuming 'dataset' is your PyTorch Dataset object
class_counts = [0] * len(dataset.classes)
for _, label in dataset:
    class_counts[label] += 1

class_weights = 1. / torch.tensor(class_counts, dtype=torch.float)
sample_weights = [class_weights[label] for _, label in dataset]
sampler = WeightedRandomSampler(sample_weights, len(dataset))

and would get synced up through load dataset

DeepForest/deepforest/main.py

Line 289 in e14bc6d

loader = self.load_dataset(csv_file=self.config["train"]["csv_file"],

and into the dataloader object

DeepForest/deepforest/main.py

Line 270 in e14bc6d

data_loader = torch.utils.data.DataLoader(