Applied Small AI Portfolio
Collection
Small-model, code-review, and deployment artifacts: EuroSAT classifier, AI-authorship review dataset, and compact coding model work. • 5 items • Updated
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
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.