Euroset Resnet50

--- license: mit tags: - image-classification - satellite-imagery - resnet50 - transfer-learning - remote-sensing datasets: - blanchon/EuroSAT_RGB language: - en base_model: - microsoft/resnet-50 --- # EuroSAT Land Cover Classifier (ResNet50) ResNet50 fine-tuned via transfer learning to classify Sentinel-2 satellite image patches into 10 land cover categories, using the EuroSAT dataset. ## Model Details - **Base model**: ResNet50, pretrained on ImageNet (`IMAGENET1K_V2` weights via torchvision) - **Training approach**: final layer trained first (backbone frozen), then full fine-tuning at lower learning rate - **Input**: 224x224 RGB images, normalized with ImageNet mean/std - **Output**: 10-class softmax (see label map below) ## Performance (EuroSAT official test split, 2,700 images) - **Overall test accuracy: 98%** - Weakest classes: PermanentCrop (F1 0.96), AnnualCrop (F1 0.96) - Strongest classes: Residential, SeaLake (F1 1.00) ## Label Map {'AnnualCrop': 0, 'Forest': 1, 'HerbaceousVegetation': 2, 'Highway': 3, 'Industrial': 4, 'Pasture': 5, 'PermanentCrop': 6, 'Residential': 7, 'River': 8, 'SeaLake': 9} ## How to Load \`\`\`python import torch import torchvision.models as models import torch.nn as nn model = models.resnet50(weights='IMAGENET1K_V2') model.fc = nn.Linear(model.fc.in_features, 10) model.load_state_dict(torch.load('eurosat_resnet50.pt', map_location='cpu')) model.eval() \`\`\` ## ScreenShot  ## Limitations - Trained on European Sentinel-2 imagery; accuracy may degrade on other regions/sensors. - 64x64 patch size limits detection of small-scale land features. - RGB-only (no multispectral bands), which loses some vegetation-discriminating signal. - Confusion observed between agricultural sub-classes (AnnualCrop/PermanentCrop/Pasture) and an unexpected River-to-AnnualCrop misclassification pattern (see confusion matrix).

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