import requests
from PIL import Image
import io

API_URL = "https://api-inference.huggingface.co/models/bsustersic/wood-identifier-resnet18"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def identify_wood(image_path):
    with open(image_path, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

# Example usage
predictions = identify_wood("wood_sample.jpg")
print(predictions)

Using Transformers Library

python

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

processor = AutoImageProcessor.from_pretrained("bsustersic/wood-identifier-resnet18")
model = AutoModelForImageClassification.from_pretrained("bsustersic/wood-identifier-resnet18")

image = Image.open("wood_sample.jpg")
inputs = processor(image, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.softmax(dim=1)

# Get top prediction
predicted_class = predictions.argmax().item()
confidence = predictions[0][predicted_class].item()
print(f"Predicted: {model.config.id2label[predicted_class]} ({confidence:.2%})")

Training Details

Training Framework: Hugging Face Transformers
Base Model: microsoft/resnet-18
Fine-tuning Dataset: Custom wood species image dataset
Image Size: 224x224 pixels
Number of Training Images: ~1036 images across 16 species

Performance

This model achieves good accuracy on wood species identification tasks. Performance may vary depending on:

Image quality and lighting conditions
Wood surface finish (raw, stained, polished)
Viewing angle and grain visibility

Limitations

Model performance depends on image quality
Best results with clear, well-lit images showing wood grain
May struggle with heavily stained or painted wood
Performance may vary on wood species not in the training set

Model Card Authors

bsustersic

Model Card Contact

For questions or issues, please open an issue on the model repository.