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README.md

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 ---
 title: Aerial Sheep Object Detection
-emoji: 😻
-colorFrom: purple
-colorTo: purple
+emoji: 🎮
+colorFrom: red
+colorTo: gray
 sdk: gradio
-sdk_version: 3.16.0
+sdk_version: 3.15.0
 app_file: app.py
 pinned: false
 ---

app.py

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+
+import json
+import gradio as gr
+import yolov5
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+app_title = "Aerial Sheep Object Detection"
+models_ids = ['keremberke/yolov5n-aerial-sheep', 'keremberke/yolov5s-aerial-sheep', 'keremberke/yolov5m-aerial-sheep']
+article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/aerial-sheep-object-detection'>dataset</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>"
+
+current_model_id = models_ids[-1]
+model = yolov5.load(current_model_id)
+
+examples = [['test_images/DJI_0039_MOV-252_jpg.rf.a9d3f531dc347711c06539af59ca7329.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0040_MOV-141_jpg.rf.b2b23a4bd86ee5f50ff4a063ab4671ca.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0043_MOV-102_jpg.rf.4f0018c8c5de23731256755050f0819a.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0043_MOV-161_jpg.rf.a2197218b8c9f58272e59d7a8c6cf493.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0043_MOV-84_jpg.rf.22ea78648b21f64c276ab348ba82cf49.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/img_373_jpg.rf.494e557cd96f79f20750ab7942c9d9c5.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep']]
+
+
+def predict(image, threshold=0.25, model_id=None):
+    # update model if required
+    global current_model_id
+    global model
+    if model_id != current_model_id:
+        model = yolov5.load(model_id)
+        current_model_id = model_id
+
+    # get model input size
+    config_path = hf_hub_download(repo_id=model_id, filename="config.json")
+    with open(config_path, "r") as f:
+        config = json.load(f)
+    input_size = config["input_size"]
+
+    # perform inference
+    model.conf = threshold
+    results = model(image, size=input_size)
+    numpy_image = results.render()[0]
+    output_image = Image.fromarray(numpy_image)
+    return output_image
+
+
+gr.Interface(
+    title=app_title,
+    description="Created by 'keremberke'",
+    article=article,
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Slider(maximum=1, step=0.01, value=0.25),
+        gr.Dropdown(models_ids, value=models_ids[-1]),
+    ],
+    outputs=gr.Image(type="pil"),
+    examples=examples,
+    cache_examples=True if examples else False,
+).launch(enable_queue=True)

requirements.txt

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+
+yolov5==7.0.5
+gradio==3.15.0
+torch
+huggingface-hub