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grano1/
$core_sample_image_main_fraction_model$
core_sample_image_main_fraction_model

Name: core_sample_image_main_fraction_model
Rating: 2.5 (51 reviews)

grano1/core_sample_image_data

microsoft/swinv2-tiny-patch4-window16-256

finetune

mit

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🪨 Core Sample Main Fraction Classifier

This multi-label classification model was fine-tuned for automated visual analysis of soil core samples, and trained / validated / tested on the grano1/core_sample_image_data dataset available via the HuggingFace eco-system. The model aims to predict one of the following four class labels - termed in accordance with DIN 4023 - from cropped core sample images (300x300 pixels):

🟤 Clay (T)
🤎 Silt (U)
🟡 Sand (S)
⚪ Gravel (G)

📊 Metrics

The following table summarizes selected metrics quantifying the model performance on the test set:

📈 Metric	Value
Categorical Cross-Entropy Loss	0.0946
Accuracy	0.9718
F1-score (macro, aggregated)	0.8765
F1-score (weighted, aggregated)	0.9712

⚙️ Hyperparameters

The following table summarizes selected hyperparameters used for training the model:

Metric	Value
Batch size	8
Optimizer	AdamW
Warm-up ratio	0.1
Metric for best model	F1-Score (Macro)
Early stopping patience	3

🚀 Usage

python

# Load the dataset
data_dataset = load_dataset("grano1/core_sample_image_data")

# Load image processor and model
processor = AutoImageProcessor.from_pretrained("grano1/core_sample_image_main_fraction_model")
model = AutoModelForImageClassification.from_pretrained("grano1/core_sample_image_main_fraction_model")
model.eval()  # Set model to evaluation mode

# Show sample features
data_dataset["test"].features

# Select sample from test set
split = "test"
sample = data_dataset[split][2]  # Pick one sample
image = sample["image"]

# Prepare input for model
inputs = processor(images=image, return_tensors="pt")

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(dim=1).item()
print(sample)

# Get predicted and true label name
id2label = model.config.id2label
label2id = model.config.label2id
predicted_label_name = id2label[predicted_class]

# Show result
print(f"✅ Predicted label: {predicted_label_name}")
print(f"🧾 True label:      {sample['HB']}")

# Display image
plt.imshow(sample['image'])
plt.axis('off')  # Hide axes
plt.show()

🔒 Limitations

The grano1/core_sample_image_data dataset includes more HB labels than the four identified by this model. This may require binning procedures, e.g., fS => S. Instructions can be found in the citation documented below.

✨ Citation

If you use this model, please cite:

bibtex

@inproceedings{Granitzer.2025,
 author = {Granitzer, Andreas-Nizar and Beck, Johannes and Leo, Johannes and Tschuchnigg, Franz},
 title = {Explainable Insight into the Vision-Based Classification of Soil Core Samples from Close-Range Images},
 pages = {228--233},
 editor = {Uzielli, Marco and Phoon, Kok-Kwang},
 booktitle = {Proceedings of the 3rd Workshop on the Future of Machine Learning in Geotechnics (3FOMLIG)},
 year = {2025},
 address = {Florence, Italy}
}