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https://github.com/fcakyon/yolov5-pip

Packaged version of ultralytics/yolov5 + many extra features
https://github.com/fcakyon/yolov5-pip

aws cli coco computer-vision deep-learning machine-learning neptune neptune-ai object-detection pip pypi python pytorch s3 ultralytics yolo yolov3 yolov4 yolov5

Last synced: 12 days ago
JSON representation

Packaged version of ultralytics/yolov5 + many extra features

Host: GitHub
URL: https://github.com/fcakyon/yolov5-pip
Owner: fcakyon
License: gpl-3.0
Created: 2021-02-10T11:36:41.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2023-10-16T10:17:06.000Z (9 months ago)
Last Synced: 2024-05-22T17:08:53.946Z (about 1 month ago)
Topics: aws, cli, coco, computer-vision, deep-learning, machine-learning, neptune, neptune-ai, object-detection, pip, pypi, python, pytorch, s3, ultralytics, yolo, yolov3, yolov4, yolov5
Language: Python
Homepage: https://pypi.org/project/yolov5/
Size: 1.58 MB
Stars: 287
Watchers: 3
Forks: 69
Open Issues: 2
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Lists

awesome-yolo-object-detection - fcakyon/yolov5-pip - pip?style=social"/> : Packaged version of ultralytics/yolov5. (Other Versions of YOLO)

README

        


  packaged ultralytics/yolov5





  pip install yolov5





  

  

  

  


  

  

  



## 
Overview




You can finally install YOLOv5 object detector using pip and integrate into your project easily.








This yolov5 package contains everything from ultralytics/yolov5 at this commit plus:




1. Easy installation via pip: pip install yolov5




2. Full CLI integration with fire package




3. COCO dataset format support (for training)




4. Full 🤗 Hub integration




5. S3 support (model and dataset upload)




6. NeptuneAI logger support (metric, model and dataset logging)




7. Classwise AP logging during experiments

## 
Install


Install yolov5 using pip (for Python >=3.7)

```console

pip install yolov5

```

## 
Model Zoo




Effortlessly explore and use finetuned YOLOv5 models with one line of code: awesome-yolov5-models





## 
Use from Python


```python

import yolov5

# load pretrained model

model = yolov5.load('yolov5s.pt')

# or load custom model

model = yolov5.load('train/best.pt')

  

# set model parameters

model.conf = 0.25  # NMS confidence threshold

model.iou = 0.45  # NMS IoU threshold

model.agnostic = False  # NMS class-agnostic

model.multi_label = False  # NMS multiple labels per box

model.max_det = 1000  # maximum number of detections per image

# set image

img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference

results = model(img)

# inference with larger input size

results = model(img, size=1280)

# inference with test time augmentation

results = model(img, augment=True)

# parse results

predictions = results.pred[0]

boxes = predictions[:, :4] # x1, y1, x2, y2

scores = predictions[:, 4]

categories = predictions[:, 5]

# show detection bounding boxes on image

results.show()

# save results into "results/" folder

results.save(save_dir='results/')

```

Train/Detect/Test/Export

- You can directly use these functions by importing them:

```python

from yolov5 import train, val, detect, export

# from yolov5.classify import train, val, predict

# from yolov5.segment import train, val, predict

train.run(imgsz=640, data='coco128.yaml')

val.run(imgsz=640, data='coco128.yaml', weights='yolov5s.pt')

detect.run(imgsz=640)

export.run(imgsz=640, weights='yolov5s.pt')

```

- You can pass any argument as input:

```python

from yolov5 import detect

img_url = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

detect.run(source=img_url, weights="yolov5s6.pt", conf_thres=0.25, imgsz=640)

```

## 
Use from CLI


You can call `yolov5 train`, `yolov5 detect`, `yolov5 val` and `yolov5 export` commands after installing the package via `pip`:

Training

- Finetune one of the pretrained YOLOv5 models using your custom `data.yaml`:

```bash

$ yolov5 train --data data.yaml --weights yolov5s.pt --batch-size 16 --img 640

                                          yolov5m.pt              8

                                          yolov5l.pt              4

                                          yolov5x.pt              2

```

- Start a training using a COCO formatted dataset:

```yaml

# data.yml

train_json_path: "train.json"

train_image_dir: "train_image_dir/"

val_json_path: "val.json"

val_image_dir: "val_image_dir/"

```

```bash

$ yolov5 train --data data.yaml --weights yolov5s.pt

```

- Train your model using [Roboflow Universe](https://universe.roboflow.com/) datasets (roboflow>=0.2.29 required):

```bash

$ yolov5 train --data DATASET_UNIVERSE_URL --weights yolov5s.pt --roboflow_token YOUR_ROBOFLOW_TOKEN

```

Where `DATASET_UNIVERSE_URL` must be in `https://universe.roboflow.com/workspace_name/project_name/project_version` format.

- Visualize your experiments via [Neptune.AI](https://neptune.ai/) (neptune-client>=0.10.10 required):

```bash

$ yolov5 train --data data.yaml --weights yolov5s.pt --neptune_project NAMESPACE/PROJECT_NAME --neptune_token YOUR_NEPTUNE_TOKEN

```

- Automatically upload weights to [Huggingface Hub](https://huggingface.co/models?other=yolov5):

```bash

$ yolov5 train --data data.yaml --weights yolov5s.pt --hf_model_id username/modelname --hf_token YOUR-HF-WRITE-TOKEN

```

- Automatically upload weights and datasets to AWS S3 (with Neptune.AI artifact tracking integration):

```bash

export AWS_ACCESS_KEY_ID=YOUR_KEY

export AWS_SECRET_ACCESS_KEY=YOUR_KEY

```

```bash

$ yolov5 train --data data.yaml --weights yolov5s.pt --s3_upload_dir YOUR_S3_FOLDER_DIRECTORY --upload_dataset

```

- Add `yolo_s3_data_dir` into `data.yaml` to match Neptune dataset with a present dataset in S3.

```yaml

# data.yml

train_json_path: "train.json"

train_image_dir: "train_image_dir/"

val_json_path: "val.json"

val_image_dir: "val_image_dir/"

yolo_s3_data_dir: s3://bucket_name/data_dir/

```

Inference

yolov5 detect command runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.

```bash

$ yolov5 detect --source 0  # webcam

                         file.jpg  # image

                         file.mp4  # video

                         path/  # directory

                         path/*.jpg  # glob

                         rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa  # rtsp stream

                         rtmp://192.168.1.105/live/test  # rtmp stream

                         http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream

```

Export

You can export your fine-tuned YOLOv5 weights to any format such as `torchscript`, `onnx`, `coreml`, `pb`, `tflite`, `tfjs`:

```bash

$ yolov5 export --weights yolov5s.pt --include torchscript,onnx,coreml,pb,tfjs

```

Classify

Train/Val/Predict with YOLOv5 image classifier:

```bash

$ yolov5 classify train --img 640 --data mnist2560 --weights yolov5s-cls.pt --epochs 1

```

```bash

$ yolov5 classify predict --img 640 --weights yolov5s-cls.pt --source images/

```

Segment

Train/Val/Predict with YOLOv5 instance segmentation model:

```bash

$ yolov5 segment train --img 640 --weights yolov5s-seg.pt --epochs 1

```

```bash

$ yolov5 segment predict --img 640 --weights yolov5s-seg.pt --source images/

```