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pythonmodel

https://github.com/fyp-fireprevention/pythonmodel

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    Found .zenodo.json file
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    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (6.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: FYP-FirePrevention
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 41 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License Citation

README.zh-CN.md

[](https://docs.ultralytics.com/zh/) | [](https://docs.ultralytics.com/ko/) | [](https://docs.ultralytics.com/ja/) | [](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Franais](https://docs.ultralytics.com/fr/) | [Espaol](https://docs.ultralytics.com/es/) | [Portugus](https://docs.ultralytics.com/pt/) | [](https://docs.ultralytics.com/hi/) | [](https://docs.ultralytics.com/ar/)
YOLOv5 CI YOLOv5 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

YOLOv5 AI Ultralytics AI YOLOv5 YOLOv5 GitHub Discord [Ultralytics Licensing](https://ultralytics.com/license)
Ultralytics GitHub Ultralytics LinkedIn Ultralytics Twitter Ultralytics YouTube Ultralytics TikTok Ultralytics Instagram Ultralytics Discord

YOLOv8

Ultralytics YOLOv8 SOTA https://github.com/ultralytics/ultralytics YOLOv8

YOLOv8

PyPI

commandline pip install ultralytics

YOLOv5

repo [**Python>=3.8.0**](https://www.python.org/) [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install ```
YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) [](https://github.com/ultralytics/yolov5/tree/master/models) YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) ```python import torch # Model model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom # Images img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ```
detect.py `detect.py` [](https://github.com/ultralytics/yolov5/tree/master/models) YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) `runs/detect` ```bash python detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```
YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) [](https://github.com/ultralytics/yolov5/tree/master/models) [](https://github.com/ultralytics/yolov5/tree/master/data) YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) YOLOv5n/s/m/l/x V100 GPU 1/2/4/6/8 [GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) `--batch-size` `--batch-size -1` YOLOv5 [](https://github.com/ultralytics/yolov5/pull/5092) batchsize V100-16GB ```bash python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 yolov5s 64 yolov5m 40 yolov5l 24 yolov5x 16 ```
- [](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) - [](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results) - [GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) - [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) - [TFLiteONNXCoreMLTensorRT](https://docs.ultralytics.com/yolov5/tutorials/model_export) - [NVIDIA Jetson](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) - [ (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) - [](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) - [/](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) - [](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) - [](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) - [](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) - [Roboflow](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) - [ClearML](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) - [Neural MagicDeepsparseYOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) - [Comet](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration)




| Roboflow | ClearML | Comet | Neural Magic | | :--------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | | YOLOv5 Roboflow | YOLOv5 ClearML | Comet YOLOv5 | Neural Magic DeepSparse YOLOv5 6 |

Ultralytics HUB

Ultralytics HUB **** YOLOv5 ****

YOLOv5

YOLOv5

YOLOv5-P5 640

- **COCO AP val** mAP@0.5:0.95 [COCO val2017](http://cocodataset.org) 5000 256 1536 - **** [COCO val2017](http://cocodataset.org) [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100batchsize 32 - **EfficientDet** [google/automl](https://github.com/google/automl) batchsize 32 - **** `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`

| |
| mAPval
50-95 | mAPval
50 |
CPU b1
ms |
V100 b1
ms |
V100 b32
ms |
(M) | FLOPs
@640 (B) | | ---------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | --------------------------------- | ---------------------------------- | ------------------------------- | ------------------ | ---------------------- | | YOLOv5n | 640 | 28.0 | 45.7 | 45 | 6.3 | 0.6 | 1.9 | 4.5 | | YOLOv5s | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | | YOLOv5m | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | | YOLOv5l | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | | YOLOv5x | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | | | | | | | | | | | | YOLOv5n6 | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | | YOLOv5s6 | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | | YOLOv5m6 | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | | YOLOv5l6 | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | | YOLOv5x6
+TTA | 1280
1536 | 55.0
55.8 | 72.7
72.7 | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- |

- 300 epochsns [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) - \*\*mAPval\*\* [COCO val2017](http://cocodataset.org)
`python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - **** COCO val [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) NMS ( 1 ms/img)
`python val.py --data coco.yaml --img 640 --task speed --batch 1` - **TTA** [](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation)
`python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`

YOLOv5 release v7.0 SOTA YOLOv5 Colab


A100 GPU COCO 640 300 epochs YOLOv5 ONNX FP32 CPU TensorRT FP16 GPU Google [Colab Pro](https://colab.research.google.com/signup) | |
| mAPbox
50-95 | mAPmask
50-95 |
300 epochs
A100 GPU |
ONNX CPU
ms |
TRT A100
ms |
(M) | FLOPs
@640 (B) | | ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | ----------------------------------------------- | ----------------------------------- | ----------------------------------- | ------------------ | ---------------------- | | [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | | [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | | [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | | [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | | [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | - SGD `lr0=0.01` `weight_decay=5e-5` 640
log https://wandb.ai/glenn-jocher/YOLOv5_v70_official - **** COCO
`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` - **** 100 [Colab Pro](https://colab.research.google.com/signup) A100 RAM NMS 1
`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` - **** FP32 ONNX FP16 TensorRT `export.py`.
`python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
 Open In Colab ### YOLOv5 COCO128-seg `--data coco128-seg.yaml` `bash data/scripts/get_coco.sh --train --val --segments` `python train.py --data coco.yaml` ```bash # GPU python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # GPU DDP python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 ``` ### COCO YOLOv5s-seg mask mAP ```bash bash data/scripts/get_coco.sh --val --segments # COCO val segments (780MB, 5000 images) python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # ``` ### YOLOv5m-seg.pt bus.jpg ```bash python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg ``` ```python model = torch.hub.load( "ultralytics/yolov5", "custom", "yolov5m-seg.pt" ) # load from PyTorch Hub (WARNING: ) ``` | ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ### YOLOv5s-seg ONNX TensorRT ```bash python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 ```

YOLOv5 release v6.2 YOLOv5 Colab


4xA100 ImageNet 90 epochs YOLOv5-cls ResNet EfficientNet ONNX FP32 CPU TensorRT FP16 GPU Google [Colab Pro](https://colab.research.google.com/signup) | |
| acc
top1 | acc
top5 |
90 epochs
4xA100 |
ONNX CPU
ms |
TensorRT V100
ms |
(M) | FLOPs
@640 (B) | | -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ----------------------------------- | ---------------------------------------- | ---------------- | ---------------------- | | [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | | [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | | [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | | [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | | [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | | | | | | | | | | | | [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | | [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | | [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | | [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | | | | | | | | | | | | [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | | [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | | [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | | [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
Table Notes () - SGD 90 epochs `lr0=0.001` `weight_decay=5e-5` 224
log https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 - **** [ImageNet-1k](https://www.image-net.org/index.php)
`python classify/val.py --data ../datasets/imagenet --img 224` - **** 100 [Colab Pro](https://colab.research.google.com/signup) V100 RAM
`python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` - **** FP32 ONNX FP16 TensorRT `export.py`
`python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
 Open In Colab ### YOLOv5 MNISTFashion-MNISTCIFAR10CIFAR100ImagenetteImagewoof ImageNet `--data` MNIST `--data mnist` ```bash # GPU python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 # GPU DDP python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 ``` ### ImageNet-1k YOLOv5m-cls ```bash bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ``` ### YOLOv5s-cls.pt bus.jpg ```bash python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg ``` ```python model = torch.hub.load( "ultralytics/yolov5", "custom", "yolov5s-cls.pt" ) # load from PyTorch Hub ``` ### YOLOv5s-clsResNet EfficientNet ONNX TensorRT ```bash python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 ```

YOLOv5

YOLOv5 YOLOv5

Ultralytics

Ultralytics GitHub Issues Discord


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Owner

  • Name: FYP-FirePrevention
  • Login: FYP-FirePrevention
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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Dependencies

utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.22.2
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
requirements.txt pypi
  • Pillow >=9.4.0
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
setup.py pypi
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==19.10.0
  • pip ==23.3
  • werkzeug >=3.0.1