Duplicate from PranomVignesh/yolov5

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README.md

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+---
+title: Yolov5
+emoji: 🏃
+colorFrom: gray
+colorTo: blue
+sdk: gradio
+sdk_version: 3.3
+app_file: app.py
+pinned: false
+license: gpl-3.0
+duplicated_from: PranomVignesh/yolov5
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+import yolov5
+from transformers import pipeline
+
+pipeline = pipeline(task="image-classification", model="PranomVignesh/Police-vs-Public")
+
+# from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+# extractor = AutoFeatureExtractor.from_pretrained("PranomVignesh/Police-vs-Public")
+# model = AutoModelForImageClassification.from_pretrained("PranomVignesh/Police-vs-Public")
+
+# Images
+# torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+# torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
+
+def yolov5_inference(
+    image
+):
+    """
+    YOLOv5 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    model = yolov5.load('./best.pt', device="cpu")
+    results = model([image], size=224)
+
+    # outputs = model(**inputs)
+    # logits = outputs.logits
+    # probabilities = torch.softmax(logits, dim=1).tolist()[0]
+
+    # classes = ['Police/Authorized Personnel', 'Public/Unauthorized Person']
+
+    # output = {name: float(prob) for name, prob in zip(classes, probabilities)}
+
+    probabilities = pipeline(image)
+    output = {p["label"]: p["score"] for p in probabilities}
+
+    return results.render()[0],output
+        
+
+inputs = gr.Image(type="pil")
+outputs = [
+    gr.Image(type="pil"),
+    gr.Label()
+]
+title = "Detection"
+description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
+
+# examples = [['zidane.jpg', 'yolov5s.pt', 640, 0.25, 0.45], ['image3.jpg', 'yolov5s.pt', 640, 0.25, 0.45]]
+demo_app = gr.Interface(
+    fn=yolov5_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    # examples=examples,
+    # cache_examples=True,
+    # live=True,
+    # theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:221d921ee575adba1f5a069f513c0c06d19815cb9a470b0bcbefaa2262d7b88a
+size 92659861

requirements.txt

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+torch
+yolov5
+transformers
+opencv-python
+gradio
+