{"id":22039388,"url":"https://github.com/mpolinowski/yolo-i-know-flowers","last_synced_at":"2025-05-07T21:43:15.040Z","repository":{"id":234831452,"uuid":"686908924","full_name":"mpolinowski/yolo-i-know-flowers","owner":"mpolinowski","description":"YOLOv8 image classifier model comparison","archived":false,"fork":false,"pushed_at":"2023-09-04T07:52:25.000Z","size":5235,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-04-21T02:03:22.450Z","etag":null,"topics":["image-classification","model-evaluation-and-selection","pytorch","yolov8"],"latest_commit_sha":null,"homepage":"https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2023-09-01--yolo-i-know-flowers/2023-09-01","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# YOLOv8 Image Classifier\n\nUsing [YOLOv8 to classifying](https://docs.ultralytics.com/tasks/classify/) an entire image into one of a set of predefined classes.\n\n| Model | size (pixels) | acc top1 | acc top5 | Speed CPU ONNX (ms) | Speed A100 TensorRT (ms) | params (M) | FLOPs (B) at 640 |\n| -- | -- | -- | -- | -- | -- | -- | -- |\n| YOLOv8n-cls | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |\n| YOLOv8s-cls | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |\n| YOLOv8m-cls | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |\n| YOLOv8l-cls | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |\n| YOLOv8x-cls | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |\n\n\n## Docker Environment\n\n* [PyTorch Jupyter](https://github.com/mpolinowski/pytorch-jupyter)\n\n\n```bash\ndocker run --gpus all -ti --rm \\\n    -v $(pwd):/opt/app -p 8888:8888 \\\n    --name pytorch-jupyter \\\n    pytorch-jupyter:latest\n```\n\n\n## Training Results\n\n* [Flower Dataset (12,297 files / 48 classes)](https://github.com/mpolinowski/tf-i-know-flowers)\n\n|         | top1_acc | top5_acc | validation [it/s] | inference [ms] | prediction preprocess [ms] | prediction inference [ms] | prediction postprocess [ms] | weights [MB] | weights (ONNX) [MB] | weights (TensorRT) [MB] |\n| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |\n| YOLOv8n | 0.792 | 0.954 | 86.50 | 0.2 | 3.600000 | 2.733333 | 0.033333 | 2.935699 | 5.731391 | 7.929402 |\n| YOLOv8s | 0.832 | 0.965 | 91.72 | 0.2 | 5.333333 | 4.500000 | 0.100000 | 9.890900 | 19.618174 | 26.030774 |\n| YOLOv8m | 0.858 | 0.969 | 87.90 | 0.4 | 0.466667 | 5.000000 | 0.066667 | 30.324455 | 60.397784 | 63.700307 |\n| YOLOv8l | 0.850 | 0.973 | 72.36 | 0.8 | 0.500000 | 7.300000 | 0.066667 | 69.331619 | 138.311538 | 166.345273 |\n| YOLOv8x | 0.861 | 0.971 | 53.22 | 1.1 | 2.166667 | 14.400000 | 0.100000 | 107.382278 | 214.371352 | 225.140141 |\n\n\n![YOLOv8 Image Classifier](./assets/curves.webp)\n\n\n![YOLOv8 Image Classifier](./assets/confusion.webp)\n\n\n![YOLOv8 Image Classifier](./assets/Model_Eval_01.webp)\n\n\n![YOLOv8 Image Classifier](./assets/Model_Eval_02.webp)\n\n\n## Model Export Options\n\n| Format | format Argument | Model | Metadata | Arguments |\n| -- | -- | -- | -- | -- |\n| PyTorch | - | yolov8n-cls.pt | ✅ | - |\n| TorchScript | torchscript | yolov8n-cls.torchscript | ✅ | imgsz, optimize |\n| ONNX | onnx | yolov8n-cls.onnx | ✅ | imgsz, half, dynamic, simplify, opset |\n| OpenVINO | openvino | yolov8n-cls_openvino_model/ | ✅ | imgsz, half |\n| TensorRT | engine | yolov8n-cls.engine | ✅ | imgsz, half, dynamic, simplify, workspace |\n| CoreML | coreml | yolov8n-cls.mlpackage | ✅ | imgsz, half, int8, nms |\n| TF SavedModel | saved_model | yolov8n-cls_saved_model/ | ✅ | imgsz, keras |\n| TF GraphDef | pb | yolov8n-cls.pb | ❌ | imgsz |\n| TF Lite | tflite | yolov8n-cls.tflite | ✅ | imgsz, half, int8 |\n| TF Edge TPU | edgetpu | 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