Resnet50 Eurosat Onnx

Classes

Note: Index order reflects id2label from the source model config. Always load it via AutoConfig.from_pretrained("cm93/resnet50-eurosat") rather than assuming alphabetical order.

Evaluation Results

Evaluated on the cm93/eurosat test split. Preprocessing applied using the source model's TimmWrapperImageProcessor via AutoImageProcessor.from_pretrained("cm93/resnet50-eurosat").

Source model reported metrics (from cm93/resnet50-eurosat):

Usage

Installation

pip install onnxruntime huggingface_hub transformers

Inference

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor, AutoConfig
from PIL import Image

# Load ONNX session
model_path = hf_hub_download("krylox/resnet50-eurosat-onnx", filename="model.onnx")
session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])

# Load processor and label map from the source model
processor = AutoImageProcessor.from_pretrained("cm93/resnet50-eurosat")
id2label  = AutoConfig.from_pretrained("cm93/resnet50-eurosat").id2label

# Preprocess and infer
image = Image.open("your_image.jpg").convert("RGB")
pixel_values = processor(images=image, return_tensors="pt")["pixel_values"].numpy().astype(np.float32)

input_name  = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
logits = session.run([output_name], {input_name: pixel_values})[0]

predicted_label = id2label[int(np.argmax(logits, axis=1)[0])]
print(f"Predicted class: {predicted_label}")

Important: Use AutoImageProcessor.from_pretrained("cm93/resnet50-eurosat") for preprocessing — the source model uses TimmWrapperImageProcessor, which may differ from standard ImageNet normalization. Using hardcoded stats will degrade accuracy significantly.

Training Details (Source Model)

Training Data

The source model was trained on EuroSAT, which contains 27,000 labeled, geo-referenced Sentinel-2 satellite image patches (JPEG, 64×64 px) across 10 LULC classes.

Intended Uses

• Land-use / land-cover classification from Sentinel-2 imagery
• Remote sensing research and benchmarking
• Deployment in non-PyTorch or resource-constrained environments via ONNX Runtime

Limitations & Biases

• Trained exclusively on European Sentinel-2 imagery; performance may degrade on other sensors or non-European regions.
• The 10-class taxonomy is coarse and may not capture fine-grained land-use distinctions.
• Inherits any biases present in the source checkpoint cm93/resnet50-eurosat.

License

MIT