Upload YOLOv8n model

config.json

@@ -0,0 +1,180 @@
+{
+  "model_type": "yolov8",
+  "scale": "n",
+  "num_classes": 80,
+  "image_size": [
+    640,
+    640
+  ],
+  "confidence_threshold": 0.25,
+  "iou_threshold": 0.45,
+  "max_detections": 300,
+  "backbone_type": "default",
+  "id2label": {
+    "0": "0",
+    "1": "1",
+    "2": "2",
+    "3": "3",
+    "4": "4",
+    "5": "5",
+    "6": "6",
+    "7": "7",
+    "8": "8",
+    "9": "9",
+    "10": "10",
+    "11": "11",
+    "12": "12",
+    "13": "13",
+    "14": "14",
+    "15": "15",
+    "16": "16",
+    "17": "17",
+    "18": "18",
+    "19": "19",
+    "20": "20",
+    "21": "21",
+    "22": "22",
+    "23": "23",
+    "24": "24",
+    "25": "25",
+    "26": "26",
+    "27": "27",
+    "28": "28",
+    "29": "29",
+    "30": "30",
+    "31": "31",
+    "32": "32",
+    "33": "33",
+    "34": "34",
+    "35": "35",
+    "36": "36",
+    "37": "37",
+    "38": "38",
+    "39": "39",
+    "40": "40",
+    "41": "41",
+    "42": "42",
+    "43": "43",
+    "44": "44",
+    "45": "45",
+    "46": "46",
+    "47": "47",
+    "48": "48",
+    "49": "49",
+    "50": "50",
+    "51": "51",
+    "52": "52",
+    "53": "53",
+    "54": "54",
+    "55": "55",
+    "56": "56",
+    "57": "57",
+    "58": "58",
+    "59": "59",
+    "60": "60",
+    "61": "61",
+    "62": "62",
+    "63": "63",
+    "64": "64",
+    "65": "65",
+    "66": "66",
+    "67": "67",
+    "68": "68",
+    "69": "69",
+    "70": "70",
+    "71": "71",
+    "72": "72",
+    "73": "73",
+    "74": "74",
+    "75": "75",
+    "76": "76",
+    "77": "77",
+    "78": "78",
+    "79": "79"
+  },
+  "label2id": {
+    "0": "0",
+    "1": "1",
+    "2": "2",
+    "3": "3",
+    "4": "4",
+    "5": "5",
+    "6": "6",
+    "7": "7",
+    "8": "8",
+    "9": "9",
+    "10": "10",
+    "11": "11",
+    "12": "12",
+    "13": "13",
+    "14": "14",
+    "15": "15",
+    "16": "16",
+    "17": "17",
+    "18": "18",
+    "19": "19",
+    "20": "20",
+    "21": "21",
+    "22": "22",
+    "23": "23",
+    "24": "24",
+    "25": "25",
+    "26": "26",
+    "27": "27",
+    "28": "28",
+    "29": "29",
+    "30": "30",
+    "31": "31",
+    "32": "32",
+    "33": "33",
+    "34": "34",
+    "35": "35",
+    "36": "36",
+    "37": "37",
+    "38": "38",
+    "39": "39",
+    "40": "40",
+    "41": "41",
+    "42": "42",
+    "43": "43",
+    "44": "44",
+    "45": "45",
+    "46": "46",
+    "47": "47",
+    "48": "48",
+    "49": "49",
+    "50": "50",
+    "51": "51",
+    "52": "52",
+    "53": "53",
+    "54": "54",
+    "55": "55",
+    "56": "56",
+    "57": "57",
+    "58": "58",
+    "59": "59",
+    "60": "60",
+    "61": "61",
+    "62": "62",
+    "63": "63",
+    "64": "64",
+    "65": "65",
+    "66": "66",
+    "67": "67",
+    "68": "68",
+    "69": "69",
+    "70": "70",
+    "71": "71",
+    "72": "72",
+    "73": "73",
+    "74": "74",
+    "75": "75",
+    "76": "76",
+    "77": "77",
+    "78": "78",
+    "79": "79"
+  },
+  "architectures": [
+    "YoloDetectionModel"
+  ]
+}

example.py

@@ -0,0 +1,47 @@
+
+# Example script for using the yolov8_n model from Hugging Face
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+# Load model and processor
+model = AutoModelForObjectDetection.from_pretrained("lkk688/yolov8n-model")
+processor = AutoImageProcessor.from_pretrained("lkk688/yolov8n-model")
+
+# Function to run inference on an image
+def detect_objects(image_path, confidence_threshold=0.25):
+    # Load image
+    image = Image.open(image_path).convert("RGB")
+    
+    # Process image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Run inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Post-process outputs
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(
+        outputs, 
+        threshold=confidence_threshold,
+        target_sizes=target_sizes
+    )[0]
+    
+    # Print results
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        print(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+        )
+    
+    return results
+
+# Example usage
+if __name__ == "__main__":
+    # Replace with your image path
+    image_path = "path/to/your/image.jpg"
+    detect_objects(image_path)

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+  "do_normalize": true,
+  "do_resize": true,
+  "do_rescale": true,
+  "image_mean": [
+    0.0,
+    0.0,
+    0.0
+  ],
+  "image_std": [
+    1.0,
+    1.0,
+    1.0
+  ],
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 640,
+    "width": 640
+  },
+  "use_letterbox": true
+}

yolov8_n.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2896e073d02095edb48db5959c79769c9dbde11d1babc53295b8957bd017974
+size 12794003