### Launch TensorBoard for Training Visualization Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Starts TensorBoard to visualize the training process. Navigate to the 'results' directory before running this command. ```bash cd results tensorboard --logdir . ``` -------------------------------- ### Start MRI Training Configuration Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Initiates the MRI denoising model training process. This command runs the main training script. ```bash python config_mri.py ``` -------------------------------- ### Create and Activate Conda Environment Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Sets up a new Conda environment named 'n2n' with Python 3.6 and installs necessary dependencies including TensorFlow GPU. ```bash conda create -n n2n python=3.6 conda activate n2n conda install tensorflow-gpu python -m pip install --upgrade pip pip install -r requirements.txt ``` -------------------------------- ### Example Training Output Log Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Shows a snippet of the expected output during the MRI training process, including epoch progress, loss, and PSNR. The 'test_db_clamped' metric represents the average PSNR on the validation set. ```text dnnlib: Running train_mri.train() on localhost... Loading training set. Loading dataset from datasets\ixi_train.pkl <...long log omitted...> Epoch 297/300: time=107.981, train_loss=0.0126064, test_db_clamped=31.72174, lr=0.000002 Epoch 298/300: time=107.477, train_loss=0.0125972, test_db_clamped=31.73622, lr=0.000001 Epoch 299/300: time=106.927, train_loss=0.0126012, test_db_clamped=31.74232, lr=0.000001 Saving final network weights. Resetting random seed and saving a bunch of example images. dnnlib: Finished train_mri.train() in 8h 59m 19s. ``` -------------------------------- ### Convert BSD300 Training Images to TFRecords Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Prepares the BSD300 dataset into a TFRecords file for training when ImageNet data is unavailable. Assumes the dataset is uncompressed in the ./datasets directory. ```bash python dataset_tool_tf.py --input-dir datasets/BSDS300-images/BSDS300/images/train --out=datasets/bsd300.tfrecords ``` -------------------------------- ### Generate Pickled Datasets for MRI Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Converts the generated PNG dataset into pickled training and validation sets. This step prepares the data for the training script. ```bash python dataset_tool_mri.py genpkl --png-dir=datasets/ixi-png --pkl-dir=datasets ``` -------------------------------- ### Train Model with Poisson Noise Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Use these commands to train a model with Poisson noise. Specify whether to use the Noise2Noise technique and the dataset to use for training. ```bash python config.py train --noise=poisson --noise2noise=true --long-train=true --train-tfrecords=datasets/imagenet_val_raw.tfrecords ``` ```bash python config.py train --noise=poisson --noise2noise=false --long-train=true --train-tfrecords=datasets/imagenet_val_raw.tfrecords ``` -------------------------------- ### Generate PNG Dataset for MRI Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Converts the IXI-T1 dataset into PNG files for training. Ensure the IXI-T1 dataset is downloaded and unpacked. ```bash # Assumes you have downloaded and untarred IXI-T1 under ~/Downloads/IXI-T1. python dataset_tool_mri.py genpng --ixi-dir=~/Downloads/IXI-T1 --outdir=datasets/ixi-png ``` -------------------------------- ### Train Model with Gaussian Noise Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Use these commands to train a model with Gaussian noise. Specify whether to use the Noise2Noise technique and the dataset to use for training. ```bash python config.py train --noise=gaussian --noise2noise=true --long-train=true --train-tfrecords=datasets/imagenet_val_raw.tfrecords ``` ```bash python config.py train --noise=gaussian --noise2noise=false --long-train=true --train-tfrecords=datasets/imagenet_val_raw.tfrecords ``` -------------------------------- ### Download Kodak Validation Set Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Downloads the Kodak Lossless True Color Image Suite dataset, which is used for validation during training. ```bash # Download the kodak validation set from http://r0k.us/graphics/kodak/ python download_kodak.py --output-dir=datasets/kodak ``` -------------------------------- ### Validate Trained Network Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Runs a validation dataset through a previously trained Noise2Noise network. Specify the dataset directory and the network snapshot file. ```bash python config.py validate --dataset-dir=datasets/kodak --network-snapshot=results/00001-autoencoder-1gpu-L-n2n/network_final.pickle ``` -------------------------------- ### Train Noise2Noise Autoencoder Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Initiates the training process for the Noise2Noise autoencoder using the ImageNet validation set with Gaussian noise. Training duration is approximately 7.5 hours on a high-end GPU. ```bash # try python config.py train --help for available options python config.py --desc='-test' train --train-tfrecords=datasets/imagenet_val_raw.tfrecords --long-train=true --noise=gaussian ``` -------------------------------- ### Validate Model with Gaussian Noise Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Validate a trained network against datasets using Gaussian noise. Ensure the noise type matches the training configuration. Replace <...> with the actual path to your network snapshot. ```bash python config.py validate --dataset-dir=datasets/kodak --noise=gaussian --network-snapshot=<.../network_final.pickle> ``` ```bash python config.py validate --dataset-dir=datasets/bsd300 --noise=gaussian --network-snapshot=<.../network_final.pickle> ``` -------------------------------- ### Validate Model with Poisson Noise Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Validate a trained network against datasets using Poisson noise. Ensure the noise type matches the training configuration. Replace <...> with the actual path to your network snapshot. ```bash python config.py validate --dataset-dir=datasets/kodak --noise=poisson --network-snapshot=<.../network_final.pickle> ``` ```bash python config.py validate --dataset-dir=datasets/bsd300 --noise=poisson --network-snapshot=<.../network_final.pickle> ``` -------------------------------- ### Generate ImageNet Validation TFRecords Source: https://github.com/nvlabs/noise2noise/blob/master/README.md Converts the ImageNet validation dataset into TFRecords format for training. Assumes ImageNet data is located at the specified path. ```bash python dataset_tool_tf.py --input-dir "/ILSVRC2012_img_val" --out=datasets/imagenet_val_raw.tfrecords ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.