You can deploy this model and start earning money today!

grano1/
$core_sample_image_secondary_fraction_model$
core_sample_image_secondary_fraction_model

Name: core_sample_image_secondary_fraction_model
Availability: InStock

grano1/core_sample_image_data

microsoft/swinv2-tiny-patch4-window16-256

finetune

mit

Views

No views yet

🪨 Core Sample Secondary 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 nine class labels - termed in accordance with DIN 4023 - from cropped core sample images (300x300 pixels):

very gravelly | gravelly (g* | g)
very sandy | sandy (s* | s)
very silty | silty (u* | u)
very clayey | clayey (t* | t)
stony (x)

📊 Metrics

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

📈 Metric	Value
Binary Cross-Entropy Loss	0.1146
Subset Accuracy	0.7606
F1-score (macro, aggregated)	0.7734
F1-score (weighted, aggregated)	0.8124
Hamming Loss	0.0363

⚙️ Hyperparameters

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

Metric	Value
Batch size	16
Optimizer	AdamW
Warm-up ratio	0.1
Metric for best model	Bin. CE
Early stopping patience	3
Threshold	0.3

🚀 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_secondary_fraction_model")
model = AutoModelForImageClassification.from_pretrained("grano1/core_sample_image_secondary_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][5]  # 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['NB']}")

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

🔒 Limitations

The grano1/core_sample_image_data dataset includes more NB labels than the nine identified by this model. This may require data cleansing procedures, e.g., s' (slightly sandy) label has been removed. 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}
}