0-ma/vit-geometric-shapes-base

Model Card for VIT Geometric Shapes Dataset Base

Training Dataset

• Repository: https://huggingface.co/datasets/0-ma/geometric-shapes

Base Model

• Repository: https://huggingface.co/models/google/vit-base-patch16-224

Accuracy

• Accuracy on dataset 0-ma/geometric-shapes [test] : 0.9269047619047619

Loading and using the model

import numpy as np
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification 
import requests
labels =  [
    "None",
    "Circle",
    "Triangle",
    "Square",
    "Pentagon",
    "Hexagon"
] 
images = [Image.open(requests.get("https://raw.githubusercontent.com/0-ma/geometric-shape-detector/main/input/exemple_circle.jpg", stream=True).raw), 
        Image.open(requests.get("https://raw.githubusercontent.com/0-ma/geometric-shape-detector/main/input/exemple_pentagone.jpg", stream=True).raw)]
feature_extractor = AutoImageProcessor.from_pretrained('0-ma/vit-geometric-shapes-base')
model = AutoModelForImageClassification.from_pretrained('0-ma/vit-geometric-shapes-base')
inputs = feature_extractor(images=images, return_tensors="pt")
logits = model(**inputs)['logits'].cpu().detach().numpy()
predictions = np.argmax(logits, axis=1)    
predicted_labels = [labels[prediction] for prediction in predictions]
print(predicted_labels)

Model generation

The model has been created using the 'train_shape_detector.py.py' of the project from the project https://github.com/0-ma/geometric-shape-detector. No external code sources were used.