ultralytics / yolov3

YOLOv3 in PyTorch > ONNX > CoreML > TFLite
https://docs.ultralytics.com
GNU Affero General Public License v3.0
10.25k stars 3.45k forks source link

YOLOv4-Tiny training #1942

Closed wiky231 closed 2 years ago

wiky231 commented 2 years ago

Search before asking

Question

Hi, I'm trying to train YOLOv4 Tiny on a custom dataset using this repo as I noticed that there is a YOLOv4-Tiny.cgf file included. However, is there any YOLOv4-Tiny.pt file for this? As I tried importing another YOLOv4-Tiny.pt file from another repo but it wasnt compatible.

And if I do not include the weights argument in the train.py code, my mAP seems to always be 0 after running for a few epochs. The following is my code: !python3 train.py --data data/roboflow.data --epochs 150 --weights '' --cfg '/content/yolov3/cfg/yolov4-tiny.cfg'

Additional

No response

github-actions[bot] commented 2 years ago

👋 Hello @axlecky, thank you for your interest in YOLOv3 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov3
$ cd yolov3
$ pip install -r requirements.txt

Environments

YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Status

CI CPU testing

If this badge is green, all YOLOv3 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

wiky231 commented 2 years ago

Hi, can anyone help please? I just need the yolov4-tiny.pt or weights file that corresponds to the cfg file provided in this repo. Would appreciate any help here.

github-actions[bot] commented 2 years ago

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv3 🚀 resources:

Access additional Ultralytics ⚡ resources:

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv3 🚀 and Vision AI ⭐!

glenn-jocher commented 1 year ago

@axlecky we do not provide YOLOv4-Tiny pretrained weights in the Ultralytics YOLOv3 repository. As for the issue with the mAP being 0, it's recommended to use pretrained weights for better convergence. You can find YOLOv4-Tiny pretrained weights from the official YOLO and Ultralytics repositories.