# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gar7mn/Geonet", dtype="auto")Quick Links
Training Details
Training Data
This model was trained on the Eurosat dataset containing Sentinel-2 satellite images available at blanchon/EuroSAT_RGB
The Eurosat dataset consists of ten classes and the a total of 27,000 images with a training set size of 16,200 images
- Annual Crop
- Forest
- Herbaceous Vegetation
- Highway
- Industrial Buildings
- Pasture
- Permanent Crop
- Residential Buildings
- River
- SeaLake
Training Procedure
- Batch size: 24
- Optimizer: AdanW
- Learning Rate: 1e-4
- Criterion: CrossEntropyLoss
- Number of Epochs: 120
Training Hyperparameters
- Training regime: [More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
- 5400 images
Metrics
Model Accuracy: 88% model Recall: 88%
[More Information Needed]
Results
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gar7mn/Geonet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")