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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+safari.jpeg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,93 @@
+import birder
+from birder.inference.detection import infer_image
+from huggingface_hub import HfApi
+
+import gradio as gr
+
+
+def get_birder_classification_models():
+    api = HfApi()
+    models = api.list_models(author="birder-project", tags="object-detection")
+    return [model.modelId.split("/")[-1] for model in models]
+
+
+def get_selected_models():
+    return [
+        "deformable_detr_boxref_coco_convnext_v2_tiny_imagenet21k",
+    ]
+
+
+def load_model_and_detect(image, model_name, min_score):
+    if len(birder.list_pretrained_models(model_name)) == 0:
+        model_name = birder.list_pretrained_models(model_name + "*")[0]
+
+    (net, (class_to_idx, signature, rgb_stats, *_)) = birder.load_pretrained_model(model_name, inference=True)
+
+    size = birder.get_size_from_signature(signature)
+    transform = birder.detection_transform(size, rgb_stats, dynamic_size=signature["dynamic"])
+    detections = infer_image(net, image, transform, score_threshold=min_score)
+
+    idx_to_class = dict(zip(class_to_idx.values(), class_to_idx.keys()))
+    label_names = [idx_to_class[i.item()] for i in detections["labels"]]
+
+    return (detections, label_names)
+
+
+def predict(image, model_name, min_score):
+    (detections, label_names) = load_model_and_detect(image, model_name, min_score)
+    if detections is None:
+        return (image, [])
+
+    annotations = []
+    boxes = detections["boxes"]
+    scores = detections["scores"]
+
+    for box, score, label in zip(boxes, scores, label_names):
+        (x1, y1, x2, y2) = box.tolist()
+        annotation = ((int(x1), int(y1), int(x2), int(y2)), f"{label} ({score:.2f})")
+        annotations.append(annotation)
+
+    return (image, annotations)
+
+
+def create_interface():
+    models = get_selected_models()
+
+    examples = [
+        ["safari.jpeg", "deformable_detr_boxref_coco_convnext_v2_tiny_imagenet21k", 0.45],
+    ]
+
+    # Create interface
+    iface = gr.Interface(
+        analytics_enabled=False,
+        deep_link=False,
+        fn=predict,
+        inputs=[
+            gr.Image(type="pil", label="Input Image"),
+            gr.Dropdown(
+                choices=models,
+                label="Select Model",
+                value=models[0] if models else None,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.5,
+                label="Minimum Score Threshold",
+                info="Only detections with confidence above this threshold will be shown",
+            ),
+        ],
+        outputs=gr.AnnotatedImage(),
+        examples=examples,
+        title="Birder Object Detection",
+        description="Select a model and upload an image or use one of the examples to get bird detections.",
+    )
+
+    return iface
+
+
+# Launch the app
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+birder
+huggingface_hub