2026人工智能一套改进YOLO的柑橘病虫害识别系统11.73-AI10.03(论文+程序代码源码)定稿.zip

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2026-09-18
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收起资源包目录

柑橘叶
- datasets
  - test
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    - labels
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  - train
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  - valid
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    - labels
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      - efbabe29-a497-439e-84f2-dbf9e2592400.txt(37 B)
      - f4e27b95-8398-4a0d-9e96-8d78ed3e236b.txt(160 B)
    - labels.cache(9.58 KB)
    - labels.cache.npy(8.47 KB)
  - video
    - 53674477c6c2e173ed5dec0c4efdcdc6.mp4(1.55 MB)
    - adaf253e2548b7dec13593e2dd062b15.mp4(324.89 KB)
  - adaf253e2548b7dec13593e2dd062b15.mp4(324.89 KB)
- yolov11
  - .github
    - ISSUE_TEMPLATE
      - bug-report.yml(2.97 KB)
      - config.yml(604 B)
      - feature-request.yml(1.83 KB)
      - question.yml(1.32 KB)
    - workflows
      - ci-testing.yml(6.76 KB)
      - cla.yml(1.6 KB)
      - docker.yml(1.72 KB)
      - format.yml(4.89 KB)
      - links.yml(3.04 KB)
      - merge-main-into-prs.yml(3.1 KB)
      - stale.yml(2.42 KB)
    - dependabot.yml(604 B)
  - .idea
    - inspectionProfiles
      - profiles_settings.xml(174 B)
    - .gitignore(190 B)
    - .name(8 B)
    - misc.xml(279 B)
    - modules.xml(273 B)
    - workspace.xml(9.31 KB)
    - yolov11.iml(486 B)
  - classify
    - predict.py(11.84 KB)
    - train.py(16.12 KB)
    - tutorial.ipynb(95.39 KB)
    - val.py(8.04 KB)
  - data
    - hyps
      - hyp.no-augmentation.yaml(1.64 KB)
      - hyp.Objects365.yaml(698 B)
      - hyp.scratch-high.yaml(1.64 KB)
      - hyp.scratch-low.yaml(1.65 KB)
      - hyp.scratch-med.yaml(1.64 KB)
      - hyp.VOC.yaml(1.15 KB)
    - images
    - scripts
      - download_weights.sh(666 B)
      - get_coco.sh(1.56 KB)
      - get_coco128.sh(643 B)
      - get_imagenet.sh(1.66 KB)
      - get_imagenet10.sh(762 B)
      - get_imagenet100.sh(766 B)
      - get_imagenet1000.sh(770 B)
    - Argoverse.yaml(2.69 KB)
    - coco.yaml(2.46 KB)
    - coco128-seg.yaml(1.88 KB)
    - coco128.yaml(1.87 KB)
    - data.yaml(194 B)
    - GlobalWheat2020.yaml(1.86 KB)
    - hat.yaml(169 B)
    - ImageNet.yaml(18.45 KB)
    - ImageNet10.yaml(956 B)
    - ImageNet100.yaml(2.61 KB)
    - ImageNet1000.yaml(18.45 KB)
    - mydata.yaml(131 B)
    - Objects365.yaml(9.01 KB)
    - SKU-110K.yaml(2.3 KB)
    - VisDrone.yaml(2.93 KB)
    - VOC.yaml(3.44 KB)
    - xView.yaml(5.07 KB)
  - models
    - hub
      - anchors.yaml(3.28 KB)
      - yolov3-spp.yaml(1.58 KB)
      - yolov3-tiny.yaml(1.24 KB)
      - yolov3.yaml(1.57 KB)
      - yolov5-bifpn.yaml(1.43 KB)
      - yolov5-fpn.yaml(1.23 KB)
      - yolov5-p2.yaml(1.69 KB)
      - yolov5-p34.yaml(1.24 KB)
      - yolov5-p6.yaml(1.74 KB)
      - yolov5-p7.yaml(2.12 KB)
      - yolov5-panet.yaml(1.42 KB)
      - yolov5l6.yaml(1.82 KB)
      - yolov5m6.yaml(1.82 KB)
      - yolov5n6.yaml(1.82 KB)
      - yolov5s-ghost.yaml(1.49 KB)
      - yolov5s-LeakyReLU.yaml(1.5 KB)
      - yolov5s-transformer.yaml(1.45 KB)
      - yolov5s6.yaml(1.82 KB)
      - yolov5x6.yaml(1.82 KB)
    - segment
      - yolov5l-seg.yaml(1.42 KB)
      - yolov5m-seg.yaml(1.42 KB)
      - yolov5n-seg.yaml(1.42 KB)
      - yolov5s-seg.yaml(1.42 KB)
      - yolov5x-seg.yaml(1.42 KB)
    - __pycache__
      - common.cpython-310.pyc(47.74 KB)
      - common.cpython-39.pyc(48.39 KB)
      - experimental.cpython-310.pyc(5.84 KB)
      - experimental.cpython-39.pyc(5.82 KB)
      - yolo.cpython-310.pyc(19.24 KB)
      - yolo.cpython-39.pyc(19.24 KB)
      - __init__.cpython-310.pyc(147 B)
      - __init__.cpython-39.pyc(146 B)
    - common.py(51.34 KB)
    - experimental.py(5.1 KB)
    - tf.py(32.76 KB)
    - yolo.py(20.56 KB)
    - __init__.py(70 B)
  - runs
    - train
      - exp8
        weights
        best.pt(54.21 MB)
        last.pt(13.78 MB)
        confusion_matrix.png(106.19 KB)
        F1_curve.png(170.16 KB)
        hyp.yaml(401 B)
        labels.jpg(174.97 KB)
        labels_correlogram.jpg(237.32 KB)
        opt.yaml(1.15 KB)
        PR_curve.png(101.81 KB)
        P_curve.png(155.39 KB)
        results.csv(3.17 KB)
        results.png(296.64 KB)
        R_curve.png(149.17 KB)
        train_batch0.jpg(418.29 KB)
        train_batch1.jpg(452.65 KB)
        train_batch2.jpg(472.57 KB)
        val_batch0_labels.jpg(589.44 KB)
        val_batch0_pred.jpg(593.21 KB)
        val_batch1_labels.jpg(577.57 KB)
        val_batch1_pred.jpg(584.6 KB)
        val_batch2_labels.jpg(61.85 KB)
        val_batch2_pred.jpg(62.23 KB)
  - segment
    - predict.py(15.93 KB)
    - train.py(34.41 KB)
    - tutorial.ipynb(41.83 KB)
    - val.py(23.81 KB)
  - utils
    - aws
      - mime.sh(846 B)
      - resume.py(1.29 KB)
      - userdata.sh(1.29 KB)
      - __init__.py(70 B)
    - docker
      - Dockerfile(2.52 KB)
      - Dockerfile-arm64(1.56 KB)
      - Dockerfile-cpu(1.8 KB)
    - flask_rest_api
      - example_request.py(391 B)
      - README.md(4 KB)
      - restapi.py(1.56 KB)
    - google_app_engine
      - additional_requirements.txt(264 B)
      - app.yaml(245 B)
      - Dockerfile(821 B)
    - loggers
      - clearml
        __pycache__
        clearml_utils.cpython-39.pyc(7.73 KB)
        __init__.cpython-39.pyc(161 B)
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- 人工智能改进YOLO的柑橘病虫害识别系统11.73-AI10.03_1.docx(2.41 MB)

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