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+YOLOv11 re-implementation using PyTorch
+
+### fix
+* fix the label size [0,1] tensor, which have two dim not adjusted size of [1,]
+    * if pic do not have object (if label is empty), the phenomenon occurs
+    * find XXX to look
+
+### Installation
+
+```
+conda create -n YOLO python=3.10.10
+conda activate YOLO
+conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
+pip install opencv-python
+pip install PyYAML
+pip install tqdm
+```
+
+### Train
+
+* Configure your dataset path in `main.py` for training
+* Run `bash main.sh $ --train` for training, `$` is number of GPUs
+* Run `nohup bash main.sh 1 --train --epochs 300 > train.log 2>&1 &` for training in background
+
+### Test
+
+* Configure your dataset path in `main.py` for testing
+* Run `python main.py --test` for testing
+
+### Results
+
+| Version | Epochs | Box mAP |                                                                              Download |
+|:-------:|:------:|--------:|--------------------------------------------------------------------------------------:|
+|  v11_n  |  600   |    38.6 |       [Model](https://github.com/jahongir7174/YOLOv11-pt/blob/master/weights/best.pt) |
+| v11_n*  |   -    |    39.2 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_n.pt) |
+| v11_s*  |   -    |    46.5 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_s.pt) |
+| v11_m*  |   -    |    51.2 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_m.pt) |
+| v11_l*  |   -    |    53.0 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_l.pt) |
+| v11_x*  |   -    |    54.3 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_x.pt) |
+
+```
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.386
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.551
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.415
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.569
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.321
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.533
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.588
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.646
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.777
+```
+
+* `*` means that it is from original repository, see reference
+* In the official YOLOv11 code, mask annotation information is used, which leads to higher performance
+
+### Dataset structure
+
+    ├── COCO 
+        ├── images
+            ├── train2017
+                ├── 1111.jpg
+                ├── 2222.jpg
+            ├── val2017
+                ├── 1111.jpg
+                ├── 2222.jpg
+        ├── labels
+            ├── train2017
+                ├── 1111.txt
+                ├── 2222.txt
+            ├── val2017
+                ├── 1111.txt
+                ├── 2222.txt
+
+#### Reference
+
+* [YOLOv11-pt](https://github.com/jahongir7174/YOLOv11-pt)