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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
| Date | Stars |
|---|---|
| 2026-07-24 | 371 |
| 2026-07-25 | 371 |
| 2026-07-28 | 371 |
| 2026-07-30 | 371 |
| 2026-07-31 | 371 |
| 2026-08-06 | 371 |
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# Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with          _Source: https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data_ > Note: the repo was developed in May 2017 on PyTorch 0.1. PyTorch was publicly announced in January 2017 and has seen tremendous changes since then. You will find: - [A script that output the mean and stddev of your image if you want to train from scratch](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/compute-mean-std.py#L28) - [Using weighted loss function](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/main_pytorch.py#L61) - [Logging your experiment](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/main_pytorch.py#L89) - [Composing data augmentations](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/main_pytorch.py#L103), also [here](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p_data_augmentation.py#L181). Note use [Pillow-SIMD](https://python-pillow.org/pillow-perf/) instead of PIL/Pillow. It is even faster than OpenCV - [Loading from a CSV that contains image path - 61 lines yeah](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p2_dataload.py#L23) - [Equivalent in Keras - 216 lines ugh](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/k_dataloader.py). Note: so much lines were needed because by default in Keras you either have the data augmentation with ImageDataGenerator or lazy loading of images with "flow_from_directory" and there is no flow_from_csv - [Model finetuning with custom PyCaffe weights](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p_neuro.py#L139) - Train_test_split, [PyTorch version](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p_model_selection.py#L4) and [Keras version](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/k_model_selection.py#L4) - [Weighted sampling training so that the model view rare cases more often](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/main_pytorch.py#L131-L140) - [Custom Sampler creation, example for the balanced sampler](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p_sampler.py) - [Saving snapshots each epoch](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/main_pytorch.py#L171) - [Loading the best snapshot for prediction](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/pytorch_predict_only.py#L83) - [Failed word embeddings experiments](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/Embedding-RNN-Autoencoder.ipynb) to [combine image and text data](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/Dual_Feed_Image_Label.ipynb) - [Combined weighted loss function (softmax for unique weather tags, BCE for multilabel tags)](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p2_loss.py#L36) - [Selecting the best F2-threshold](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p2_metrics.py#L38) via stochastic search at the end of each epoch to [maximize validation score](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/526128239a6abcbb32fbf5b34ed8cc7a3cd87c4e/src/p2_validation.py#L49). This is then saved along model parameter. - [CNN-RNN combination (work in progress)](https://github.com/mratsim/Amazon_Forest_Computer_Vision/blob/master/src/p3_neuroRNN.py#L10)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dbcb0727419b71a6, topic:deep-learning, topic:pytorch, readme:train from scratch
matched fp:dbcb0727419b71a6, topic:data-augmentation, readme:data augmentation
matched fp:dbcb0727419b71a6, topic:computer-vision, name:computer vision, desc:computer vision