Update README.md

README.md

@@ -1,5 +1,45 @@
----
-license: other
-license_name: nvidia-derivative
-license_link: LICENSE
----
+---
+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] : ???
+
+# 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/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)