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Eurosat Simplenet

Small PyTorch CNN for 10-class EuroSAT land-use classification.

Sourcehuggingface.co/yava-code/eurosat-simplenet/tree/03a0ea98707d9249c5ad12e49d528ea7567ae4de
KindOtherVersionRevision 03a0ea9

Model source

Kind
Other
File size
9.52 MB
Version
vRevision 03a0ea9
Updated
Sep 16, 2026
Downloads
0
Source
Hugging Face

Source description

SimpleNet EuroSAT Land-Use Classifier

Small PyTorch CNN for 10-class EuroSAT land-use classification.

This model is used by the Gradio Space:

Model Details

FieldValue
ArchitectureFour Conv-BN-ReLU-Pool blocks plus dense classifier
Parameters2,492,170
InputRGB image resized to 64 x 64
Output10 EuroSAT land-use classes
RuntimeCPU-friendly PyTorch inference

Classes:

AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake

Usage

import torch
from huggingface_hub import hf_hub_download

from model import SimpleNet, CLASS_NAMES

weights = hf_hub_download(
    repo_id="yava-code/eurosat-simplenet",
    filename="simple_net_v1.pth",
)

model = SimpleNet(num_classes=len(CLASS_NAMES))
model.load_state_dict(torch.load(weights, map_location="cpu"))
model.eval()

Training Notes

  • Dataset: EuroSAT RGB land-use imagery
  • Optimizer: Adam
  • Augmentations: flips, rotations, color jitter
  • Goal: compact educational classifier, not production remote-sensing model

Deployment Notes

For the Build Small Hackathon Space, the same state dict is converted to float16 chunks so the app can be reviewed without Git LFS write permissions in the organization repo. The runtime reconstructs the bytes, casts tensors back to float32, and loads the explicit SimpleNet architecture.

Limits

This model is trained for EuroSAT-style RGB tiles. It should not be used for production geospatial decisions without target-domain validation, calibration, and uncertainty handling.

Sources

1 source
Hugging FacePrimaryVersion 03a0ea98707d9249c5ad12e49d528ea7567ae4de
huggingface.co/yava-code/eurosat-simplenet/tree/03a0ea98707d9249c5ad12e49d528ea7567ae4de

Verified Sep 16

Model artifacts

2 artifacts

best_model.pth

pth · 9.52 MB · SHA-256 2df3471bdea7…dec0 · Hugging Face

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simple_net_v1.pth

pth · 9.52 MB · SHA-256 2df3471bdea7…dec0 · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 16huggingface.co/yava-code/eurosat-simplenet

--- language: en license: mit library_name: pytorch tags: - pytorch - image-classification - satellite-imagery - computer-vision - eurosat - small-models - local-first datasets: - torchgeo/eurosat metrics: - accuracy pipeline_tag: image-classification --- # SimpleNet EuroSAT Land-Use Classifier Small PyTorch CNN for 10-class EuroSAT land-use classification. This model is used by the Gradio Space: - Hackathon Space: https://huggingface.co/spaces/build-small-hackathon/EuroSATFieldScout - Personal Space: https://huggingface.co/spaces/yava-code/eurosat-field-scout ## Model Details | Field | Value | | --- | --- | | Architecture | Four Conv-BN-ReLU-Pool blocks plus dense classifier | | Parameters | 2,492,170 | | Input | RGB image resized to 64 x 64 | | Output | 10 EuroSAT land-use classes | | Runtime | CPU-friendly PyTorch inference | Classes: `AnnualCrop`, `Forest`, `HerbaceousVegetation`, `Highway`, `Industrial`, `Pasture`, `PermanentCrop`, `Residential`, `River`, `SeaLake` ## Usage ```python import torch from huggingface_hub import hf_hub_download from model import SimpleNet, CLASS_NAMES weights = hf_hub_download( repo_id="yava-code/eurosat-simplenet", filename="simple_net_v1.pth", ) model = SimpleNet(num_classes=len(CLASS_NAMES)) model.load_state_dict(torch.load(weights, map_location="cpu")) model.eval() ``` ## Training Notes - Dataset: EuroSAT RGB land-use imagery - Optimizer: Adam - Augmentations: flips, rotations, color jitter - Goal: compact educational classifier, not production remote-sensing model ## Deployment Notes For the Build Small Hackathon Space, the same state dict is converted to float16 chunks so the app can be reviewed without Git LFS write permissions in the organization repo. The runtime reconstructs the bytes, casts tensors back to float32, and loads the explicit `SimpleNet` architecture. ## Limits This model is trained for EuroSAT-style RGB tiles. It should not be used for production geospatial decisions without target-domain validation, calibration, and uncertainty handling.

NoteSep 16huggingface.co/yava-code/eurosat-simplenet

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo yava-code/eurosat-simplenet