Create README.md

README.md

@@ -1,310 +1,12 @@
-# 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 (with NTU)
-
-[`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
-
-```
-@inproceedings{wang2023yolov7,
-  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},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2023}
-}
-```
-
-```
-@article{wang2023designing,
-  title={Designing Network Design Strategies Through Gradient Path Analysis},
-  author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
-  journal={Journal of Information Science and Engineering},
-  year={2023}
-}
-```
-
-
-## 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>
-
-YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)
-
-<div align="center">
-    <a href="./">
-        <img src="./figure/yolov7_city.jpg" width="80%"/>
-    </a>
-</div>
-
-YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)
-
-<div align="center">
-    <a href="./">
-        <img src="./figure/yolov7_3d.jpg" width="30%"/>
-    </a>
-    <a href="./">
-        <img src="./figure/yolov7_lidar.jpg" width="30%"/>
-    </a>
-    <a href="./">
-        <img src="./figure/yolov7_road.jpg" width="30%"/>
-    </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>
+---
+title: YoloCustom
+emoji: 🚀
+colorFrom: blue
+colorTo: green
+sdk: gradio  # or "streamlit", "static", etc., depending on your app
+sdk_version: "4.44.0"  # Check the latest version if using Gradio
+app_file: app.py  # Replace with your main script (e.g., interfacetest.py?)
+pinned: false
+---
+# YoloCustom Space
+This is a custom YOLO implementation.