AgML VS pytorch-grad-cam

Compare AgML vs pytorch-grad-cam and see what are their differences.

AgML

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations. (by Project-AgML)
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AgML pytorch-grad-cam
1 5
156 9,530
5.8% -
7.5 5.0
15 days ago 17 days ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

AgML

Posts with mentions or reviews of AgML. We have used some of these posts to build our list of alternatives and similar projects.

pytorch-grad-cam

Posts with mentions or reviews of pytorch-grad-cam. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-13.

What are some alternatives?

When comparing AgML and pytorch-grad-cam you can also consider the following projects:

gluon-cv - Gluon CV Toolkit

Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

ScanRefer - [ECCV 2020] ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]

cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

Entity - EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation

tf-keras-vis - Neural network visualization toolkit for tf.keras

labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.

Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

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

pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers