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README.md

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+---
+license: agpl-3.0
+tags:
+- object-detection
+- computer-vision
+- yolov10
+datasets:
+- detection-datasets/coco
+inference: false
+---
+
+### Model Description
+[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458v1)
+
+- arXiv: https://arxiv.org/abs/2405.14458v1
+- github: https://github.com/THU-MIG/yolov10
+
+### Installation
+```
+pip install git+https://github.com/THU-MIG/yolov10.git
+```
+
+### Training and validation
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10.from_pretrained('jameslahm/yolov10b')
+# Training
+model.train(...)
+# after training, one can push to the hub
+model.push_to_hub("your-hf-username/yolov10-finetuned")
+
+# Validation
+model.val(...)
+```
+
+### Inference
+
+Here's an end-to-end example showcasing inference on a cats image:
+
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10.from_pretrained('jameslahm/yolov10b')
+source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+model.predict(source=source, save=True)
+```
+which shows:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/628ece6054698ce61d1e7be3/E24iLZLtBXva9uXXgrYmo.png)
+
+### BibTeX Entry and Citation Info
+ ```
+@article{wang2024yolov10,
+  title={YOLOv10: Real-Time End-to-End Object Detection},
+  author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
+  journal={arXiv preprint arXiv:2405.14458},
+  year={2024}
+}
+```

config.json

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+{
+  "model": "yolov10b.yaml",
+  "names": {
+    "0": "person",
+    "1": "bicycle",
+    "2": "car",
+    "3": "motorcycle",
+    "4": "airplane",
+    "5": "bus",
+    "6": "train",
+    "7": "truck",
+    "8": "boat",
+    "9": "traffic light",
+    "10": "fire hydrant",
+    "11": "stop sign",
+    "12": "parking meter",
+    "13": "bench",
+    "14": "bird",
+    "15": "cat",
+    "16": "dog",
+    "17": "horse",
+    "18": "sheep",
+    "19": "cow",
+    "20": "elephant",
+    "21": "bear",
+    "22": "zebra",
+    "23": "giraffe",
+    "24": "backpack",
+    "25": "umbrella",
+    "26": "handbag",
+    "27": "tie",
+    "28": "suitcase",
+    "29": "frisbee",
+    "30": "skis",
+    "31": "snowboard",
+    "32": "sports ball",
+    "33": "kite",
+    "34": "baseball bat",
+    "35": "baseball glove",
+    "36": "skateboard",
+    "37": "surfboard",
+    "38": "tennis racket",
+    "39": "bottle",
+    "40": "wine glass",
+    "41": "cup",
+    "42": "fork",
+    "43": "knife",
+    "44": "spoon",
+    "45": "bowl",
+    "46": "banana",
+    "47": "apple",
+    "48": "sandwich",
+    "49": "orange",
+    "50": "broccoli",
+    "51": "carrot",
+    "52": "hot dog",
+    "53": "pizza",
+    "54": "donut",
+    "55": "cake",
+    "56": "chair",
+    "57": "couch",
+    "58": "potted plant",
+    "59": "bed",
+    "60": "dining table",
+    "61": "toilet",
+    "62": "tv",
+    "63": "laptop",
+    "64": "mouse",
+    "65": "remote",
+    "66": "keyboard",
+    "67": "cell phone",
+    "68": "microwave",
+    "69": "oven",
+    "70": "toaster",
+    "71": "sink",
+    "72": "refrigerator",
+    "73": "book",
+    "74": "clock",
+    "75": "vase",
+    "76": "scissors",
+    "77": "teddy bear",
+    "78": "hair drier",
+    "79": "toothbrush"
+  },
+  "task": "detect"
+}