Init

.gitignore

@@ -0,0 +1,14 @@
+__pycache__/
+data/dataests/
+/runs
+
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tiff
+*.gif
+*.JPG
+*.webp
+
+*.pt

README.md

@@ -1 +1,288 @@
-# Food-Recognition-App-Model
+# Official YOLOv7
+
+Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/yolov7-trainable-bag-of-freebies-sets-new/real-time-object-detection-on-coco)](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)
+<a href="https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2207.02696-B31B1B.svg)](https://arxiv.org/abs/2207.02696)
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/performance.png" width="79%"/>
+    </a>
+</div>
+
+## Web Demo
+
+- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)
+
+## Performance 
+
+MS COCO
+
+| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
+| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
+| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
+|  |  |  |  |  |  |  |
+| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
+| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
+| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
+| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
+
+## Installation
+
+Docker environment (recommended)
+<details><summary> <b>Expand</b> </summary>
+
+``` shell
+# create the docker container, you can change the share memory size if you have more.
+nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
+
+# apt install required packages
+apt update
+apt install -y zip htop screen libgl1-mesa-glx
+
+# pip install required packages
+pip install seaborn thop
+
+# go to code folder
+cd /yolov7
+```
+
+</details>
+
+## Testing
+
+[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)
+
+``` shell
+python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
+```
+
+You will get the results:
+
+```
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51206
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69730
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.55521
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38453
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.63765
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.68772
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
+```
+
+To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`
+
+## Training
+
+Data preparation
+
+``` shell
+bash scripts/get_coco.sh
+```
+
+* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip) 
+
+Single GPU training
+
+``` shell
+# train p5 models
+python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
+
+# train p6 models
+python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
+```
+
+Multiple GPU training
+
+``` shell
+# train p5 models
+python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
+
+# train p6 models
+python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
+```
+
+## Transfer learning
+
+[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt)
+
+Single GPU finetuning for custom dataset
+
+``` shell
+# finetune p5 models
+python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
+
+# finetune p6 models
+python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
+```
+
+## Re-parameterization
+
+See [reparameterization.ipynb](tools/reparameterization.ipynb)
+
+## Inference
+
+On video:
+``` shell
+python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
+```
+
+On image:
+``` shell
+python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
+```
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/horses_prediction.jpg" width="59%"/>
+    </a>
+</div>
+
+
+## Export
+
+**Pytorch to CoreML (and inference on MacOS/iOS)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+
+**Pytorch to ONNX with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+```shell
+python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
+        --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
+```
+
+**Pytorch to TensorRT with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7trt.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+
+```shell
+wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
+python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
+git clone https://github.com/Linaom1214/tensorrt-python.git
+python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
+```
+
+**Pytorch to TensorRT another way** <a href="https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <details><summary> <b>Expand</b> </summary>
+
+
+```shell
+wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
+python export.py --weights yolov7-tiny.pt --grid --include-nms
+git clone https://github.com/Linaom1214/tensorrt-python.git
+python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
+
+# Or use trtexec to convert ONNX to TensorRT engine
+/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16
+```
+
+</details>
+
+Tested with: Python 3.7.13, Pytorch 1.12.0+cu113
+
+## Pose estimation
+
+[`code`](https://github.com/WongKinYiu/yolov7/tree/pose) [`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt)
+
+See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb).
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/pose.png" width="39%"/>
+    </a>
+</div>
+
+
+## Instance segmentation
+
+[`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)
+
+See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instance.ipynb).
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/mask.png" width="59%"/>
+    </a>
+</div>
+
+## Instance segmentation
+
+[`code`](https://github.com/WongKinYiu/yolov7/tree/u7/seg) [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt)
+
+YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)
+
+| Model | Test Size | AP<sup>box</sup> | AP<sub>50</sub><sup>box</sup> | AP<sub>75</sub><sup>box</sup> | AP<sup>mask</sup> | AP<sub>50</sub><sup>mask</sup> | AP<sub>75</sub><sup>mask</sup> |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+| **YOLOv7-seg** | 640 | **51.4%** | **69.4%** | **55.8%** | **41.5%** | **65.5%** | **43.7%** |
+
+## Anchor free detection head
+
+[`code`](https://github.com/WongKinYiu/yolov7/tree/u6) [`yolov7-u6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-u6.pt)
+
+YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)
+
+| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> |
+| :-- | :-: | :-: | :-: | :-: |
+| **YOLOv7-u6** | 640 | **52.6%** | **69.7%** | **57.3%** |
+
+
+## Citation
+
+```
+@article{wang2022yolov7,
+  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
+  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
+  journal={arXiv preprint arXiv:2207.02696},
+  year={2022}
+}
+```
+
+```
+@article{wang2022designing,
+  title={Designing Network Design Strategies Through Gradient Path Analysis},
+  author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
+  journal={arXiv preprint arXiv:2211.04800},
+  year={2022}
+}
+```
+
+
+## Teaser
+
+Yolov7-semantic & YOLOv7-panoptic & YOLOv7-caption
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/tennis.jpg" width="24%"/>
+    </a>
+    <a href="./">
+        <img src="./figure/tennis_semantic.jpg" width="24%"/>
+    </a>
+    <a href="./">
+        <img src="./figure/tennis_panoptic.png" width="24%"/>
+    </a>
+    <a href="./">
+        <img src="./figure/tennis_caption.png" width="24%"/>
+    </a>
+</div>
+
+
+## Acknowledgements
+
+<details><summary> <b>Expand</b> </summary>
+
+* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
+* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
+* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
+* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
+* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
+* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
+* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
+* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
+* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)
+* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
+
+</details>

Test_Verison.py

@@ -0,0 +1,20 @@
+import torch
+
+# 檢查CUDA是否可用
+cuda_available = torch.cuda.is_available()
+print("CUDA 可用:", cuda_available)
+
+# 檢查CUDA版本
+if cuda_available:
+    cuda_version = torch.version.cuda
+    print("CUDA 版本:", cuda_version)
+
+# 檢查cuDNN是否可用
+if cuda_available:
+    cudnn_available = torch.backends.cudnn.enabled
+    print("cuDNN 可用:", cudnn_available)
+
+# 檢查cuDNN版本
+if cuda_available and cudnn_available:
+    cudnn_version = torch.backends.cudnn.version()
+    print("cuDNN 版本:", cudnn_version)

cfg/baseline/r50-csp.yaml

@@ -0,0 +1,49 @@
+# parameters
+nc: 46  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [12,16, 19,36, 40,28]  # P3/8
+  - [36,75, 76,55, 72,146]  # P4/16
+  - [142,110, 192,243, 459,401]  # P5/32
+
+# CSP-ResNet backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Stem, [128]],  # 0-P1/2
+   [-1, 3, ResCSPC, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 2-P3/8
+   [-1, 4, ResCSPC, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 4-P3/8
+   [-1, 6, ResCSPC, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 6-P3/8
+   [-1, 3, ResCSPC, [1024]],  # 7
+  ]
+
+# CSP-Res-PAN head
+head:
+  [[-1, 1, SPPCSPC, [512]], # 8
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [5, 1, Conv, [256, 1, 1]], # route backbone P4
+   [[-1, -2], 1, Concat, [1]],
+   [-1, 2, ResCSPB, [256]], # 13
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [3, 1, Conv, [128, 1, 1]], # route backbone P3
+   [[-1, -2], 1, Concat, [1]],
+   [-1, 2, ResCSPB, [128]], # 18
+   [-1, 1, Conv, [256, 3, 1]],
+   [-2, 1, Conv, [256, 3, 2]],
+   [[-1, 13], 1, Concat, [1]],  # cat
+   [-1, 2, ResCSPB, [256]], # 22
+   [-1, 1, Conv, [512, 3, 1]],
+   [-2, 1, Conv, [512, 3, 2]],
+   [[-1, 8], 1, Concat, [1]],  # cat
+   [-1, 2, ResCSPB, [512]], # 26
+   [-1, 1, Conv, [1024, 3, 1]],
+
+   [[19,23,27], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]