metadata

base_model: nvidia/mit-b0
datasets:
  - 0-ma/geometric-shapes
license: other
metrics:
  - accuracy
pipeline_tag: image-classification

Model Card for Mit-B0 Geometric Shapes Dataset

Training Dataset

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

Base Model

Repository: https://huggingface.co/models/nvidia/mit-b0

Accuracy

Accuracy on dataset 0-ma/geometric-shapes [test] : 0.8628571428571429

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/mit-b0-geometric-shapes')
model = AutoModelForImageClassification.from_pretrained('0-ma/mit-b0-geometric-shapes')
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)

License

The license for this model can be found here.