metadata
base_model: microsoft/resnet-50
datasets:
- 0-ma/geometric-shapes
license: apache-2.0
metrics:
- accuracy
pipeline_tag: image-classification
Model Card for Resnet Geometric Shapes Dataset
Training Dataset
Base Model
Accuracy
- Accuracy on dataset 0-ma/geometric-shapes [test] : 0.7828571428571428
Loading and using the model
import numpy as np
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
import requests
labels = [
"Only text",
"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/resnet-geometric-shapes')
model = AutoModelForImageClassification.from_pretrained('0-ma/resnet-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)