What are the requirements?

[martinenkoEduard]

What are the requirements?

[gdiasbruno]

Same doubt here

[jingjjjjjie]

hi guys, have you found out the requirements yet?

[alantim]

I used the requirements from ultralytics/yolov5 and added some missing packages:

# YOLOv5 requirements
# Usage: pip install -r requirements.txt

# Base ------------------------------------------------------------------------
matplotlib>=3.3
numpy>=1.23.5
opencv-python>=4.1.1
pillow>=10.3.0
psutil  # system resources
PyYAML>=5.3.1
requests>=2.32.0
scipy>=1.4.1
thop>=0.1.1  # FLOPs computation
torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
torchvision>=0.9.0
tqdm>=4.64.0
tensorboard
# ultralytics>=8.2.34  # https://ultralytics.com
# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012

# Logging ---------------------------------------------------------------------
# tensorboard>=2.4.1
# clearml>=1.2.0
# comet

# Plotting --------------------------------------------------------------------
pandas>=1.1.4
seaborn>=0.11.0

# Export ----------------------------------------------------------------------
# coremltools>=6.0  # CoreML export
# onnx>=1.10.0  # ONNX export
# onnx-simplifier>=0.4.1  # ONNX simplifier
# nvidia-pyindex  # TensorRT export
# nvidia-tensorrt  # TensorRT export
# scikit-learn<=1.1.2  # CoreML quantization
# tensorflow>=2.4.0,<=2.13.1  # TF exports (-cpu, -aarch64, -macos)
# tensorflowjs>=3.9.0  # TF.js export
# openvino-dev>=2023.0  # OpenVINO export

# Deploy ----------------------------------------------------------------------
# setuptools>=70.0.0 # Snyk vulnerability fix
# tritonclient[all]~=2.24.0

# Extras ----------------------------------------------------------------------
ipython  # interactive notebook
cython
# mss  # screenshots
# albumentations>=1.0.3
# pycocotools>=2.0.6  # COCO mAP

I also made an environment.yaml file, in case you want to install using conda:

# reasons you might want to use `environment.yaml` instead of `requirements.txt`:
# - pip installs packages in a loop, without ensuring dependencies across all packages
#   are fulfilled simultaneously, but conda achieves proper dependency control across
#   all packages
# - conda allows for installing packages without requiring certain compilers or
#   libraries to be available in the system, since it installs precompiled binaries

name: myenv

channels:
  - pytorch
  - conda-forge
  - defaults

# it is strongly recommended to specify versions of packages installed through conda
# to avoid situation when version-unspecified packages install their latest major
# versions which can sometimes break things

# current approach below keeps the dependencies in the same major versions across all
# users, but allows for different minor and patch versions of packages where backwards
# compatibility is usually guaranteed

dependencies:
  - python=3.10
  - matplotlib=3.*
  - numpy=1.*
  - opencv=4.*
  - pillow=10.*
  - pytorch=2.*
  - psutil
  - pyyaml=5.*
  - requests=2.*
  - scipy=1.*
  - pytorch
  - torchvision
  - tqdm=4.*
  - pandas=1*
  - seaborn=0.*
  - tensorboard
  - pip>=23
  - pip:
      - thop

  # ------ For Evaluation ----- #
  - cython
  - ipython