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--- |
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base_model: microsoft/resnet-50 |
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datasets: |
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- 0-ma/geometric-shapes |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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--- |
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# Model Card for Resnet Geometric Shapes Dataset |
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## Training Dataset |
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- **Repository:** https://huggingface.co/datasets/0-ma/geometric-shapes |
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## Base Model |
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- **Repository:** https://huggingface.co/models/microsoft/resnet-50 |
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## Accuracy |
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- Accuracy on dataset 0-ma/geometric-shapes [test] : 0.7828571428571428 |
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# Loading and using the model |
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import numpy as np |
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from PIL import Image |
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from transformers import AutoImageProcessor, AutoModelForImageClassification |
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import requests |
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labels = [ |
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"None", |
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"Circle", |
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"Triangle", |
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"Square", |
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"Pentagon", |
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"Hexagon" |
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] |
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images = [Image.open(requests.get("https://raw.githubusercontent.com/0-ma/geometric-shape-detector/main/input/exemple_circle.jpg", stream=True).raw), |
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Image.open(requests.get("https://raw.githubusercontent.com/0-ma/geometric-shape-detector/main/input/exemple_pentagone.jpg", stream=True).raw)] |
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feature_extractor = AutoImageProcessor.from_pretrained('0-ma/resnet-geometric-shapes') |
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model = AutoModelForImageClassification.from_pretrained('0-ma/resnet-geometric-shapes') |
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inputs = feature_extractor(images=images, return_tensors="pt") |
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logits = model(**inputs)['logits'].cpu().detach().numpy() |
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predictions = np.argmax(logits, axis=1) |
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predicted_labels = [labels[prediction] for prediction in predictions] |
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print(predicted_labels) |
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