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YOLOv7 Object Detection on Aquarium Images

This project aims to train the YOLOv7 object detection model on a custom dataset comprising diverse aquarium images containing fish and aquatic creatures.

Dataset

  • Download Dataset: Aquarium Dataset on Roboflow
  • API Download: 
    !pip install roboflow
from roboflow import Roboflow

rf = Roboflow(api_key="YOUR_API_KEY")
project = rf.workspace("roboflow-100").project("aquarium-qlnqy")
dataset = project.version(2).download("yolov7")

Training

  • Model Used: YOLOv7 with 100 epochs
  • Pretrained Weights: YOLOv7.pt
  • Training Command: 
    !python train.py --workers 1 --device 0 --batch-size 16 --epochs 100 --img 640 640 --data /content/drive/MyDrive/aquarium_dataset/data.yaml --hyp data/hyp.scratch.custom.yaml --name yolov7-custom --weights yolov7.pt

Performance

YOLOv7 Models Comparison on MS COCO

Model Test Size APtest AP50test AP75test Batch 1 FPS Batch 32 Avg. Time
YOLOv7 640 51.4% 69.7% 55.9% 161 fps 2.8 ms
YOLOv7-X 640 53.1% 71.2% 57.8% 114 fps 4.3 ms
YOLOv7-W6 1280 54.9% 72.6% 60.1% 84 fps 7.6 ms
YOLOv7-E6 1280 56.0% 73.5% 61.2% 56 fps 12.3 ms
YOLOv7-D6 1280 56.6% 74.0% 61.8% 44 fps 15.0 ms
YOLOv7-E6E 1280 56.8% 74.4% 62.1% 36 fps 18.7 ms

Detection Results

Usage

Detection Command

  • Detecting using Best Model: 
    !python detect.py --weights /content/drive/MyDrive/yolov7/runs/train/yolov7-custom2/weights/best.pt --conf 0.2 --img-size 640 --source /content/drive/MyDrive/aquarium_dataset/test/images/

Contributors