init

.gitignore

@@ -239,3 +239,8 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+# ignore Datasets
+**/datasets/
+
+**/bus.jpg
+

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.13 (models)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (models)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10.12 WSL (Ubuntu-22.04): (/home/awin/python/venvs/model/bin/python)" project-jdk-type="Python SDK" />
 </project>

.idea/models.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/.venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.13 (models)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10.12 WSL (Ubuntu-22.04): (/home/awin/python/venvs/model/bin/python)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

README.md

@@ -1,2 +1,5 @@
 # models
 
+---
+
+这是一个用于训练各种模型的项目。

yolov11/datasets/coco8/LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+Program specifies that a certain numbered version of the GNU General
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+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
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+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
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+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

yolov11/datasets/coco8/README.md

@@ -0,0 +1,12 @@
+# Ultralytics COCO8 Dataset
+
+Ultralytics COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train
+2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models,
+or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet
+diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
+
+This dataset is intended for use with Ultralytics YOLOv8.
+
+Docs: https://docs.ultralytics.com
+Community: https://community.ultralytics.com
+GitHub: https://github.com/ultralytics/ultralytics

yolov11/datasets/coco8/labels/train/000000000009.txt

@@ -0,0 +1,8 @@
+45 0.479492 0.688771 0.955609 0.5955
+45 0.736516 0.247188 0.498875 0.476417
+50 0.637063 0.732938 0.494125 0.510583
+45 0.339438 0.418896 0.678875 0.7815
+49 0.646836 0.132552 0.118047 0.0969375
+49 0.773148 0.129802 0.0907344 0.0972292
+49 0.668297 0.226906 0.131281 0.146896
+49 0.642859 0.0792187 0.148063 0.148062

yolov11/datasets/coco8/labels/train/000000000025.txt

@@ -0,0 +1,2 @@
+23 0.770336 0.489695 0.335891 0.697559
+23 0.185977 0.901608 0.206297 0.129554

yolov11/datasets/coco8/labels/train/000000000030.txt

@@ -0,0 +1,2 @@
+58 0.519219 0.451121 0.39825 0.75729
+75 0.501188 0.592138 0.26 0.456192

yolov11/datasets/coco8/labels/train/000000000034.txt

@@ -0,0 +1 @@
+22 0.346211 0.493259 0.689422 0.892118

yolov11/datasets/coco8/labels/val/000000000036.txt

@@ -0,0 +1,2 @@
+25 0.475759 0.414523 0.951518 0.672422
+0 0.671279 0.617945 0.645759 0.726859

yolov11/datasets/coco8/labels/val/000000000042.txt

@@ -0,0 +1 @@
+16 0.606687 0.341381 0.544156 0.51

yolov11/datasets/coco8/labels/val/000000000049.txt

@@ -0,0 +1,9 @@
+17 0.597835 0.63755 0.342283 0.36886
+17 0.324291 0.64808 0.219711 0.3164
+0 0.620039 0.5939 0.172415 0.14608
+0 0.385525 0.58557 0.14937 0.12586
+0 0.328898 0.70199 0.0313386 0.06714
+58 0.622546 0.89961 0.185932 0.09446
+0 0.760577 0.69423 0.0285564 0.05486
+0 0.510709 0.69215 0.0187927 0.04682
+0 0.929554 0.67602 0.0388451 0.01844

yolov11/datasets/coco8/labels/val/000000000061.txt

@@ -0,0 +1,5 @@
+0 0.445688 0.480615 0.075125 0.117295
+0 0.640086 0.471742 0.0508281 0.0814344
+20 0.643211 0.558852 0.129828 0.097623
+20 0.459703 0.592121 0.22175 0.159242
+0 0.435383 0.45832 0.0534531 0.111025

yolov11/runs/detect/train/args.yaml

@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: yolo12n.pt
+data: coco8.yaml
+epochs: 100
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: train
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: runs/detect/train

yolov11/runs/detect/train/results.csv

@@ -0,0 +1,101 @@
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yolov11/yolov12初始化.py

@@ -0,0 +1,19 @@
+from ultralytics import YOLO
+
+# Load a COCO-pretrained YOLO12n model
+model = YOLO("yolo12n.pt")
+
+# Train the model on the COCO8 example dataset for 100 epochs
+results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+print(results)
+
+# Evaluate the model's performance on the validation set
+results = model.val()
+print(results)
+
+# Perform object detection on an image using the model
+results = model("https://ultralytics.com/images/bus.jpg")
+print(results)
+
+# Export the model to ONNX format
+success = model.export(format="onnx")