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pipeline:window:cryolo [2019/09/14 23:59] twagner [Configuration] |
pipeline:window:cryolo [2019/09/17 13:40] twagner [Advanced parameters] |
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< | < | ||
+ | |||
You can find more technical details in our paper: | You can find more technical details in our paper: | ||
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===== Tutorials ===== | ===== Tutorials ===== | ||
- | Depending what you want to do, you can follow one of these Tutorials: | + | Depending what you want to do, you can follow one of these self-contained |
- I would like to train a model from scratch for picking my particles | - I would like to train a model from scratch for picking my particles | ||
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===== Picking particles - Using a model trained for your data ===== | ===== Picking particles - Using a model trained for your data ===== | ||
+ | This tutorial explains you how to train a model specific for you dataset. | ||
- | |||
- | ==== Data preparation ==== | ||
If you followed the installation instructions, | If you followed the installation instructions, | ||
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source activate cryolo | source activate cryolo | ||
</ | </ | ||
- | + | ==== Data preparation ==== | |
- | In the following I will assume that your image data is in the folder '' | + | {{page>pipeline: |
- | + | ||
- | The next step is to create training data. To do so, we have to pick single particles manually in several micrographs. Ideally, the micrographs are picked to completion. [[: | + | |
- | One may ask how many micrographs have to be picked? It depends! Typically 10 micrographs are a good start. However, that number may increase / decrease due to several factors: | + | |
- | * A very heterogenous background could make it necessary to pick more micrographs. | + | |
- | * When you refine a general model, you might need to pick less micrographs. | + | |
- | * If your micrograph is only sparsely decorated, you may need to pick more micrographs. | + | |
- | + | ||
- | We recommend that you start with 10 micrographs, | + | |
- | + | ||
- | {{:pipeline: | + | |
- | To create your training data, crYOLO is shipped with a tool called " | + | |
- | + | ||
- | Start the box manager with the following command: | + | |
- | < | + | |
- | cryolo_boxmanager.py | + | |
- | </ | + | |
- | + | ||
- | Now press //File -> Open image folder// and the select the '' | + | |
- | + | ||
- | * LEFT MOUSE BUTTON: Place a box | + | |
- | * HOLD LEFT MOUSE BUTTON: Move a box | + | |
- | * CONTROL + LEFT MOUSE BUTTON: Remove a box | + | |
- | + | ||
- | You can change the box size in the main window, by changing the number in the text field labeled //Box size://. Press //Set// to apply it to all picked particles. __For picking, you should the use minimum sized square which encloses your particle.__ | + | |
- | + | ||
- | If you finished picking from your micrographs, | + | |
- | Create a new directory called '' | + | |
- | + | ||
- | Now create a third folder with the name '' | + | |
==== Start crYOLO ==== | ==== Start crYOLO ==== | ||
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==== Configuration ==== | ==== Configuration ==== | ||
{{page> | {{page> | ||
- | ==== Training ==== | ||
- | Now you are ready to train the model. In case you have multiple GPUs, you should first select a free GPU. The following command will show the status of all GPUs: | + | < |
- | <code> | + | <div style=" |
- | nvidia-smi | + | < |
- | </code> | + | </div> |
- | For this tutorial, we assume that you have either a single GPU or want to use GPU 0. Therefore we add '-g 0' after each command below. However, if you have multiple (e.g GPU 0 and GPU 1) you could also use both by adding '-g 0 1' after each command. | + | </html> |
- | Navigate to the folder with '' | ||
- | **Train your network with 3 warmup epochs:** | + | {{page> |
- | < | + | ==== Training ==== |
- | cryolo_train.py -c config.json -w 3 -g 0 | + | |
- | </ | + | |
- | + | ||
- | The final model will be called '' | + | |
- | + | ||
- | The training stops when the " | + | |
- | + | ||
- | < | + | |
- | cryolo_train.py -c config.json -w 3 -g 0 -e 15 | + | |
- | </ | + | |
- | to the training | + | {{page> |
==== Picking ==== | ==== Picking ==== | ||
{{page> | {{page> | ||
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==== Visualize the results ==== | ==== Visualize the results ==== | ||
{{page> | {{page> | ||
+ | |||
+ | ==== Evaluate your results ==== | ||
+ | {{page> | ||
===== Picking particles - Without training using a general model ===== | ===== Picking particles - Without training using a general model ===== | ||
Here you can find how to apply the general models we trained for you. If you would like to train your own general model, please see our extra wiki page: [[: | Here you can find how to apply the general models we trained for you. If you would like to train your own general model, please see our extra wiki page: [[: | ||
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Our general models can be found and downloaded here: [[howto: | Our general models can be found and downloaded here: [[howto: | ||
+ | If you followed the installation instructions, | ||
+ | |||
+ | < | ||
+ | source activate cryolo | ||
+ | </ | ||
==== Start crYOLO ==== | ==== Start crYOLO ==== | ||
{{page> | {{page> | ||
==== Configuration==== | ==== Configuration==== | ||
- | The next step is to create a configuration file. Type: | + | In the GUI choose the //config// action. Fill in your target box size and leave the // |
+ | |||
+ | {{ : | ||
+ | |||
+ | [[: | ||
+ | |||
+ | * General model trained for low-pass filtered images : Select //filter// " | ||
+ | * General model trained for JANNI-denoised images: Select //filter// " | ||
+ | * General model for negative stain images: Select filter " | ||
+ | |||
+ | < | ||
+ | <div style=" | ||
+ | < | ||
+ | </ | ||
+ | </ | ||
+ | |||
+ | |||
+ | |||
+ | <hidden **Create the configuration file using the command line**> | ||
+ | In the following I assume that you target box size is 220. Please adapt if necessary. | ||
+ | |||
+ | For the general **[[: | ||
< | < | ||
- | touch config.json | + | cryoloo.py |
</ | </ | ||
- | Open the file with your preferred editor. | + | For the general model trained with **neural-network denoised cryo images** (with [[: |
- | + | <code> | |
- | There are two general **[[: | + | cryoloo.py |
- | === CryoEM images === | + | |
- | For the general **[[: | + | |
- | <hidden **config.json for low-pass filtered cryo-images**> | + | |
- | <code json config.json> | + | |
- | { | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | } | + | |
- | } | + | |
- | </ | + | |
- | </ | + | |
- | < | + | |
- | For the general model trained with **neural-network denoised cryo images** (with JANNI' | + | |
- | <hidden **config.json | + | |
- | <code json config.json> | + | |
- | { | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | "filter": | + | |
- | } | + | |
- | } | + | |
</ | </ | ||
- | You can download the file '' | + | For the general model for **negative stain data** please |
- | </ | + | <code> |
- | < | + | cryoloo.py |
- | In all cases please set the value in the //" | + | |
- | + | ||
- | === Negative stain images === | + | |
- | For the general model for **negative stain data** please | + | |
- | <hidden **config.json for negative stain images**> | + | |
- | <code json config.json> | + | |
- | { | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | " | + | |
- | } | + | |
- | } | + | |
</ | </ | ||
</ | </ | ||
- | |||
- | Please set the value in the //" | ||
==== Picking ==== | ==== Picking ==== | ||
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However, the fine tune mode is still somewhat experimental and we will update this section if see more advantages or disadvantages. | However, the fine tune mode is still somewhat experimental and we will update this section if see more advantages or disadvantages. | ||
- | ===== Start crYOLO | + | If you followed the installation instructions, |
+ | |||
+ | < | ||
+ | source activate cryolo | ||
+ | </ | ||
+ | |||
+ | ==== Data preparation ==== | ||
+ | {{page> | ||
+ | |||
+ | ==== Start crYOLO ==== | ||
{{page> | {{page> | ||
==== Configuration ==== | ==== Configuration ==== | ||
+ | {{page> | ||
+ | |||
+ | {{ : | ||
+ | Furthermore, | ||
+ | |||
+ | < | ||
+ | <div style=" | ||
+ | < | ||
+ | </ | ||
+ | </ | ||
+ | |||
+ | <hidden **Create the configuration file using the command line:**> | ||
- | You can use almost | + | I assume your box files for training are in the folder '' |
< | < | ||
- | " | + | cryoloo.py config config_cryolo.json 160 --train_image_folder train_image --train_annot_folder train_annot --pretrained_weights |
- | [...] | + | |
- | "pretrained_weights": | + | |
- | [...] | + | |
- | " | + | |
- | [...] | + | |
- | } | + | |
</ | </ | ||
+ | |||
+ | To get a full description of all available options type: | ||
+ | < | ||
+ | cryoloo.py config -h | ||
+ | </ | ||
+ | |||
+ | If you want to specify seperate validation folders you can use the %%--%%valid_image_folder and %%--%%valid_annot_folder options: | ||
+ | |||
+ | < | ||
+ | cryoloo.py config config_cryolo.json 160 --train_image_folder train_image --train_annot_folder train_annot --pretrained_weights gmodel_phosnet_20190516.h5 --valid_image_folder valid_img --valid_annot_folder valid_annot | ||
+ | </ | ||
+ | |||
+ | </ | ||
==== Training ==== | ==== Training ==== | ||
- | In comparison | + | |
+ | Now you are ready to train the model. In case you have multiple GPUs, you should | ||
< | < | ||
- | cryolo_train.py | + | nvidia-smi |
</ | </ | ||
+ | |||
+ | For this tutorial, we assume that you have either a single GPU or want to use GPU 0. | ||
+ | |||
+ | In the GUI choose the action //train//. In the //" | ||
+ | {{ : | ||
+ | |||
+ | In the //" | ||
+ | {{ : | ||
+ | <note important> | ||
+ | The number of layers to fine tune (specified by layers_fine_tune in the //" | ||
+ | </ | ||
+ | |||
<note tip> | <note tip> | ||
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The fine tune mode is especially useful if you want to [[downloads: | The fine tune mode is especially useful if you want to [[downloads: | ||
</ | </ | ||
+ | |||
+ | <hidden **Run training with the command line**> | ||
+ | In comparison to the training from scratch, you can skip the warm up training ( -w 0 ). Moreover you have to add the // | ||
+ | |||
+ | < | ||
+ | cryolo_train.py -c config.json -w 0 -g 0 --fine_tune -lft 2 | ||
+ | </ | ||
+ | </ | ||
+ | |||
==== Picking ==== | ==== Picking ==== | ||
{{page> | {{page> | ||
+ | ==== Visualize the results ==== | ||
+ | {{page> | ||
+ | ==== Evaluate your results ==== | ||
+ | {{page> | ||
===== Picking filaments - Using a model trained for your data ===== | ===== Picking filaments - Using a model trained for your data ===== | ||
Since version 1.1.0 crYOLO supports picking filaments. | Since version 1.1.0 crYOLO supports picking filaments. | ||
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{{: | {{: | ||
+ | |||
+ | If you followed the installation instructions, | ||
+ | |||
+ | < | ||
+ | source activate cryolo | ||
+ | </ | ||
+ | |||
==== Data preparation ==== | ==== Data preparation ==== | ||
- | {{ : | + | {{ : |
The first step is to create the training data for your model. Right now, you have to use the e2helixboxer.py for this: | The first step is to create the training data for your model. Right now, you have to use the e2helixboxer.py for this: | ||
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After tracing your training data in e2helixboxer, | After tracing your training data in e2helixboxer, | ||
+ | |||
+ | For projects with roughly 20 filaments per image we successfully trained on 40 images (=> 800 filaments). | ||
+ | |||
+ | ==== Start crYOLO ==== | ||
+ | {{page> | ||
+ | |||
+ | |||
==== Configuration ==== | ==== Configuration ==== | ||
{{page> | {{page> | ||
- | ==== Training ==== | ||
- | In principle, there is not much difference in training crYOLO for filament picking and particle picking. For project with roughly 20 filaments per image we successfully trained on 40 images (=> 800 filaments). However, in our experience the warm-up phase and training need a little bit more time: | ||
- | **Train your network with 10 warm up epochs:** | + | < |
+ | <div style=" | ||
+ | < | ||
+ | </ | ||
+ | </ | ||
- | < | ||
- | cryolo_train.py -c config.json -w 10 -g 0 -e 10 | ||
- | </ | ||
- | The final model will be called '' | + | {{page> |
+ | ==== Training ==== | ||
+ | |||
+ | {{page> | ||
==== Picking ==== | ==== Picking ==== | ||
+ | Select the action prediction and fill all arguments in the “Required arguments” tab: | ||
+ | {{ : | ||
- | The biggest difference in picking filaments with crYOLO is during prediction. However, there are just three additional parameters needed: | + | Now select the " |
- | * //- -filament//: Option that tells crYOLO that you want to predict filaments | + | {{ :pipeline:window:cryolo_filament.png?700 |}} |
- | * //-fw//: Filament width (pixels) | + | |
- | * //-bd//: Inter-Box distance (pixels). | + | |
+ | Press the start button to start the picking. The directory '' | ||
+ | |||
+ | You can find a detailed description [[: | ||
+ | |||
+ | <hidden **Run prediction in commmand line**> | ||
Let's assume you want to pick a filament with a width of 100 pixels (-fw 100). The box size is 200x200 and you want a 90% overlap (-bd 20). Moreover, you wish that each filament has at least 6 boxes (-mn 6). The micrographs are in the '' | Let's assume you want to pick a filament with a width of 100 pixels (-fw 100). The box size is 200x200 and you want a 90% overlap (-bd 20). Moreover, you wish that each filament has at least 6 boxes (-mn 6). The micrographs are in the '' | ||
< | < | ||
- | cryolo_predict.py -c config.json -w model.h5 -i full_data --filament -fw 100 -bd 20 -o boxes/ -g 0 -mn 6 | + | cryolo_predict.py -c cryolo_config.json -w cryolo_model.h5 -i full_data --filament -fw 100 -bd 20 -o boxes/ -g 0 -mn 6 |
</ | </ | ||
+ | </ | ||
+ | |||
- | The directory '' | ||
==== Visualize the results ==== | ==== Visualize the results ==== | ||
{{page> | {{page> | ||
- | |||
- | ===== Evaluate your results ===== | ||
- | <note warning> | ||
- | Unfortunately, | ||
- | </ | ||
- | The evaluation tool allows you, based on your validation data, to get statistics about your training. | ||
- | If you followed the tutorial, the validation data are selected randomly. With crYOLO 1.1.0 a run file for each training is created and saved into the folder runfiles/ in your project directory. This run file contains which files were selected for validation, and you can run your evaluation as follows: | ||
- | < | ||
- | cryolo_evaluation.py -c config.json -w model.h5 -r runfiles/ | ||
- | </ | ||
- | |||
- | The result looks like this: | ||
- | {{: | ||
- | |||
- | The table contains several statistics: | ||
- | * AUC: Area under curve of the precision-recall curve. Overall summary statistics. Perfect classifier = 1, Worst classifier = 0 | ||
- | * Topt: Optimal confidence threshold with respect to the F1 score. It might not be ideal for your picking, as the F1 score weighs recall and precision equally. However in SPA, recall is often more important than the precision. | ||
- | * R (Topt): Recall using the optimal confidence threshold. | ||
- | * R (0.3): Recall using a confidence threshold of 0.3. | ||
- | * R (0.2): Recall using a confidence threshold of 0.2. | ||
- | * P (Topt): Precision using the optimal confidence threshold. | ||
- | * P (0.3): Precision using a confidence threshold of 0.3. | ||
- | * P (0.2): Precision using a confidence threshold of 0.2. | ||
- | * F1 (Topt): Harmonic mean of precision and recall using the optimal confidence threshold. | ||
- | * F1 (0.3): Harmonic mean of precision and recall using a confidence threshold of 0.3. | ||
- | * F1 (0.2): Harmonic mean of precision and recall using a confidence threshold of 0.2. | ||
- | * IOU (Topt): Intersection over union of the auto-picked particles and the corresponding ground-truth boxes. The higher, the better -- evaluated with the optimal confidence threshold. | ||
- | * IOU (0.3): Intersection over union of the auto-picked particles and the corresponding ground-truth boxes. The higher, the better -- evaluated with a confidence threshold of 0.3. | ||
- | * IOU (0.2): Intersection over union of the auto-picked particles and the corresponding ground-truth boxes. The higher, the better -- evaluated with a confidence threshold of 0.2. | ||
- | |||
- | If the training data consists of multiple folders, then evaluation will be done for each folder separately. | ||
- | Furthermore, | ||
- | ===== Advanced parameters ===== | ||
- | During **training** (// | ||
- | * // | ||
- | * // | ||
- | * // | ||
- | * // | ||
- | * // | ||
- | * //%%-%%lft NUM_LAYER_FINETUNE//: | ||
- | During **picking** (// | ||
- | * //-t CONFIDENCE_THRESHOLD//: | ||
- | * //-d DISTANCE_IN_PIXEL//: | ||
- | * //-pbs PREDICTION_BATCH_SIZE//: | ||
- | * // | ||
- | * // | ||
- | * // | ||
- | * // | ||
- | * //-sr SEARCH_RANGE_FACTOR//: | ||
- | |||
===== Help ===== | ===== Help ===== |