FasterRCNN VS detecto

Compare FasterRCNN vs detecto and see what are their differences.

FasterRCNN

Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras. (by trzy)
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FasterRCNN detecto
2 1
108 607
- -
2.0 0.0
about 1 year ago 9 months ago
Python Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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FasterRCNN

Posts with mentions or reviews of FasterRCNN. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-28.

detecto

Posts with mentions or reviews of detecto. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-11.
  • large images?
    2 projects | /r/pytorch | 11 Jun 2022
    I used https://github.com/alankbi/detecto and successfully trained a model for surface defects using some publicly posted images. https://github.com/dgleba/ml635e/blob/main/e03cast.ipynb

What are some alternatives?

When comparing FasterRCNN and detecto you can also consider the following projects:

PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

easy-yolov7 - This a clean and easy-to-use implementation of YOLOv7 in PyTorch, made with ❤️ by Theos AI.

Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0

pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Detic - Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

faster-rcnn.pytorch - A faster pytorch implementation of faster r-cnn

fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

ml635e

autogluon - Fast and Accurate ML in 3 Lines of Code

sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots