Open SiduoLi2020 opened 11 months ago
Hi,
thanks for getting in touch :-)
How to use directional method "PREDICTED"?
how did you train the crYOLO model? You need to train with filament annotations to get a filament model which can use "PREDICTED". The general model is only for single particles.
"How to use directional method "PREDICTED"? Also I want to know weather PREDICTED is more precise than CONVOLUTION
I didn't do a systematic comparison, but PREDICTED should be more flexible regarding the filament shape. CONVOLUTION expects that the filament has certain width.
By the way, tutorials document of Install napari and the boxmanager plugin is incorrect. I cannot install napari by conda while the command pip install napari can work. :)
Can you tell me what kind of error you saw? I just redid the installation as described in the tutorial and its working on my side.
Best, Thorsten
Thanks for your response! Perhaps the napari is cause of all problems.
Can you tell me what kind of error you saw? I just redid the installation as described in the tutorial and its working on my side.
I still cannot install napari by conda. Here is the log.
conda create -y -n napari-cryolo -c conda-forge python=3.10 napari=0.4.17 pyqt pip
An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. If your current network has https://repo.anaconda.com blocked, please file a support request with your network engineering team. 'https//repo.anaconda.com/pkgs/main/linux-64'
I also tried download napari in dependent , it stopped in solving enviromnet.
conda install -c conda-forge napari=0.4.17`
Collecting package metadata (current_repodata.json): / DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/linux-64/current_repodata.json HTTP/1.1" 304 0 | DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/noarch/current_repodata.json HTTP/1.1" 200 None
DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/linux-64/current_repodata.json HTTP/1.1" 200 None - DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/noarch/current_repodata.json HTTP/1.1" 304 0 - DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/linux-64/current_repodata.json HTTP/1.1" 304 0
DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/noarch/current_repodata.json HTTP/1.1" 304 0 done
Solving environment: unsuccessful initial attempt using frozen solve. Retrying with flexible solve.
Solving environment: unsuccessful attempt using repodata from current_repodata.json, retrying with next repodata source.
Collecting package metadata (repodata.json): | DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443 DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/noarch/repodata.json HTTP/1.1" 200 None DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/linux-64/repodata.json HTTP/1.1" 200 None DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/linux-64/repodata.json HTTP/1.1" 304 0
DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/linux-64/repodata.json HTTP/1.1" 304 0 | DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/noarch/repodata.json HTTP/1.1" 304 0 / DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/noarch/repodata.json HTTP/1.1" 304 0 done
Solving environment: \
I'm not sure if all this is caused by network issues or the absence of this package on conda:
conda install napari`
PackagesNotFoundError: The following packages are not available from current channels:
- napari
Finally I try to download it by pip, thanksfully it works. (napari 0.4.18)
how did you train the crYOLO model? You need to train with filament annotations to get a filament model which can use "PREDICTED". The general model is only for single particles.
Is it because I deleted so many things that cryolo can't recognize the training data as filament?
Maybe the napari installed from pip have some bug. I cannot load .cox data into CRYOLO when i start trainning.
#####################################################
Important debugging information.
In case of any problems, please provide this information.
#####################################################
/home/em/anaconda3/envs/cryolo/bin/cryolo_gui.py train
-c config_cryolo.json
-w 5
-nc 16
--gpu_fraction 1.0
-e 10
-lft 2
--seed 10
#####################################################
###############################################
New version of crYOLO available
Local version: 1.8.4
Latest version: 1.9.6
More information here:
https://cryolo.readthedocs.io/en/latest/changes.html
###############################################
###############################################
The following training image sizes were detected:
4096 x 4096 ( N: 19 )
crYOLO will train in mode: SQUARE
###############################################
Reading old CBOX format file
2023-09-28 22:37:44.408501: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.
Using TensorFlow backend.
2023-09-28 22:37:45.391510: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000000000 Hz
2023-09-28 22:37:45.392759: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55c37ef32190 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-09-28 22:37:45.392783: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2023-09-28 22:37:45.396331: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2023-09-28 22:37:45.632035: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55c37e424c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-09-28 22:37:45.632077: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2023-09-28 22:37:45.633273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1666] Found device 0 with properties:
name: NVIDIA GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
pciBusID: 0000:1a:00.0
2023-09-28 22:37:45.633341: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2023-09-28 22:37:45.645550: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2023-09-28 22:37:45.674337: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2023-09-28 22:37:45.674665: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2023-09-28 22:37:45.675267: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11
2023-09-28 22:37:45.676324: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2023-09-28 22:37:45.676471: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2023-09-28 22:37:45.678536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1794] Adding visible gpu devices: 0
2023-09-28 22:37:45.678600: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2023-09-28 22:37:46.174525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1206] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-09-28 22:37:46.174567: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] 0
2023-09-28 22:37:46.174575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1225] 0: N
2023-09-28 22:37:46.176901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1351] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6624 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:1a:00.0, compute capability: 7.5)
Traceback (most recent call last):
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2895, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: '_CoordinateZ'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/CoordsIO.py", line 230, in read_cbox_boxfile
z=starfile['cryolo']['_CoordinateZ'][i],
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/pandas/core/frame.py", line 2906, in __getitem__
indexer = self.columns.get_loc(key)
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2897, in get_loc
raise KeyError(key) from err
KeyError: '_CoordinateZ'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/em/anaconda3/envs/cryolo/bin/cryolo_gui.py", line 8, in <module>
sys.exit(_main_())
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/cryolo_main.py", line 455, in _main_
Gooey(
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/gooey/python_bindings/gooey_decorator.py", line 134, in <lambda>
return lambda *args, **kwargs: func(*args, **kwargs)
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/cryolo_main.py", line 424, in main
train.main(args)
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/train.py", line 516, in main
parse_dict = preprocess.parse_annotation(
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/preprocessing.py", line 150, in parse_annotation
filaments = CoordsIO.read_cbox_boxfile(boxpath, int(cell_h))
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/CoordsIO.py", line 265, in read_cbox_boxfile
return read_cbox_boxfile_old(path)
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/CoordsIO.py", line 273, in read_cbox_boxfile_old
boxreader = np.atleast_2d(np.genfromtxt(path))
File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/numpy/lib/npyio.py", line 2103, in genfromtxt
raise ValueError(errmsg)
ValueError: Some errors were detected !
Line #3 (got 2 columns instead of 1)
Line #13 (got 5 columns instead of 1)
Line #14 (got 5 columns instead of 1)
Line #15 (got 5 columns instead of 1)
Line #16 (got 5 columns instead of 1)
Line #17 (got 5 columns instead of 1)
Line #18 (got 5 columns instead of 1)
Line #19 (got 5 columns instead of 1)
Line #20 (got 5 columns instead of 1)
Line #21 (got 5 columns instead of 1)
Line #22 (got 5 columns instead of 1)
Line #23 (got 5 columns instead of 1)
Line #24 (got 5 columns instead of 1)
Line #25 (got 5 columns instead of 1)
Line #26 (got 5 columns instead of 1)
Line #27 (got 5 columns instead of 1)
Line #28 (got 5 columns instead of 1)
Line #38 (got 5 columns instead of 1)
Line #39 (got 5 columns instead of 1)
Line #40 (got 5 columns instead of 1)
Line #41 (got 5 columns instead of 1)
Line #42 (got 5 columns instead of 1)
Line #43 (got 5 columns instead of 1)
Line #44 (got 5 columns instead of 1)
Line #45 (got 5 columns instead of 1)
Line #46 (got 5 columns instead of 1)
Line #47 (got 5 columns instead of 1)
Line #48 (got 5 columns instead of 1)
Line #49 (got 5 columns instead of 1)
Line #50 (got 5 columns instead of 1)
Line #51 (got 5 columns instead of 1)
Line #52 (got 5 columns instead of 1)
Line #53 (got 5 columns instead of 1)
Line #54 (got 5 columns instead of 1)
Line #55 (got 5 columns instead of 1)
Line #56 (got 5 columns instead of 1)
Line #57 (got 5 columns instead of 1)
Line #58 (got 5 columns instead of 1)
Line #59 (got 5 columns instead of 1)
Line #60 (got 5 columns instead of 1)
Line #61 (got 5 columns instead of 1)
Line #62 (got 5 columns instead of 1)
Line #63 (got 5 columns instead of 1)
Line #64 (got 5 columns instead of 1)
Line #65 (got 5 columns instead of 1)
Line #66 (got 5 columns instead of 1)
Line #67 (got 5 columns instead of 1)
Line #68 (got 5 columns instead of 1)
Line #69 (got 5 columns instead of 1)
Line #70 (got 5 columns instead of 1)
Line #71 (got 5 columns instead of 1)
Line #72 (got 5 columns instead of 1)
Line #73 (got 5 columns instead of 1)
Line #74 (got 5 columns instead of 1)
Line #75 (got 5 columns instead of 1)
Line #76 (got 5 columns instead of 1)
Line #77 (got 5 columns instead of 1)
Line #78 (got 5 columns instead of 1)
Line #79 (got 5 columns instead of 1)
Line #80 (got 5 columns instead of 1)
Line #81 (got 5 columns instead of 1)
Line #82 (got 5 columns instead of 1)
Line #83 (got 5 columns instead of 1)
Line #84 (got 5 columns instead of 1)
Line #85 (got 5 columns instead of 1)
Line #86 (got 5 columns instead of 1)
Line #87 (got 5 columns instead of 1)
Line #88 (got 5 columns instead of 1)
Line #89 (got 5 columns instead of 1)
Line #90 (got 5 columns instead of 1)
Line #91 (got 5 columns instead of 1)
Line #92 (got 5 columns instead of 1)
Line #93 (got 5 columns instead of 1)
Line #94 (got 5 columns instead of 1)
Line #95 (got 5 columns instead of 1)
Line #96 (got 5 columns instead of 1)
Line #97 (got 5 columns instead of 1)
Line #98 (got 5 columns instead of 1)
Line #99 (got 5 columns instead of 1)
Line #100 (got 5 columns instead of 1)
Line #101 (got 5 columns instead of 1)
Line #102 (got 5 columns instead of 1)
Line #103 (got 5 columns instead of 1)
Line #104 (got 5 columns instead of 1)
Line #105 (got 5 columns instead of 1)
Line #106 (got 5 columns instead of 1)
Line #107 (got 5 columns instead of 1)
Line #108 (got 5 columns instead of 1)
Line #109 (got 5 columns instead of 1)
Line #110 (got 5 columns instead of 1)
Line #111 (got 5 columns instead of 1)
Line #112 (got 5 columns instead of 1)
Line #113 (got 5 columns instead of 1)
Line #114 (got 5 columns instead of 1)
Line #115 (got 5 columns instead of 1)
Line #116 (got 5 columns instead of 1)
Line #117 (got 5 columns instead of 1)
Line #118 (got 5 columns instead of 1)
Line #119 (got 5 columns instead of 1)
I check the .cox file:
data_global
_cbox_format_version 1.0
data_filament_vertices
loop_
_CoordinateX #1
_CoordinateY #2
_filamentid #3
_Width #4
_Height #5
1167.069071 1648.989192 0.000000 500.000000 500.000000
221.764288 235.665864 0.000000 500.000000 500.000000
1709.229167 1384.859914 1.000000 500.000000 500.000000
2585.026245 175.425853 1.000000 500.000000 500.000000
1139.265989 370.047426 2.000000 500.000000 500.000000
1343.155256 193.961241 2.000000 500.000000 500.000000
2306.995427 1964.090786 3.000000 500.000000 500.000000
4007.617267 1556.312252 3.000000 500.000000 500.000000
1199.506000 2724.041690 4.000000 500.000000 500.000000
1273.647551 4021.518843 4.000000 500.000000 500.000000
471.992024 2612.829363 5.000000 500.000000 500.000000
967.813651 3993.715761 5.000000 500.000000 500.000000
1662.890697 2413.573943 6.000000 500.000000 500.000000
2793.549359 2700.872455 6.000000 500.000000 500.000000
921.475181 2571.124740 7.000000 500.000000 500.000000
2093.838466 3919.574210 7.000000 500.000000 500.000000
data_cryolo
loop_
_CoordinateX #1
_CoordinateY #2
_filamentid #3
_Width #4
_Height #5
1398.989192 917.069071 0.000000 500.000000 500.000000
1281.212248 838.293672 0.000000 500.000000 500.000000
1163.435304 759.518274 0.000000 500.000000 500.000000
1045.658360 680.742875 0.000000 500.000000 500.000000
927.881416 601.967476 0.000000 500.000000 500.000000
810.104472 523.192078 0.000000 500.000000 500.000000
692.327528 444.416679 0.000000 500.000000 500.000000
574.550584 365.641281 0.000000 500.000000 500.000000
456.773640 286.865882 0.000000 500.000000 500.000000
338.996696 208.090484 0.000000 500.000000 500.000000
221.219752 129.315085 0.000000 500.000000 500.000000
103.442808 50.539686 0.000000 500.000000 500.000000
-14.334136 -28.235712 0.000000 500.000000 500.000000
1134.859914 1459.229167 1.000000 500.000000 500.000000
1013.916508 1546.808875 1.000000 500.000000 500.000000
892.973102 1634.388583 1.000000 500.000000 500.000000
772.029696 1721.968290 1.000000 500.000000 500.000000
651.086290 1809.547998 1.000000 500.000000 500.000000
530.142884 1897.127706 1.000000 500.000000 500.000000
409.199478 1984.707414 1.000000 500.000000 500.000000
288.256072 2072.287122 1.000000 500.000000 500.000000
167.312666 2159.866830 1.000000 500.000000 500.000000
46.369259 2247.446538 1.000000 500.000000 500.000000
-74.574147 2335.026245 1.000000 500.000000 500.000000
120.047426 889.265989 2.000000 500.000000 500.000000
-56.038759 1093.155256 2.000000 500.000000 500.000000
1714.090786 2056.995427 3.000000 500.000000 500.000000
1680.109242 2198.713914 3.000000 500.000000 500.000000
1646.127697 2340.432400 3.000000 500.000000 500.000000
1612.146153 2482.150887 3.000000 500.000000 500.000000
1578.164608 2623.869374 3.000000 500.000000 500.000000
1544.183064 2765.587860 3.000000 500.000000 500.000000
1510.201519 2907.306347 3.000000 500.000000 500.000000
1476.219975 3049.024834 3.000000 500.000000 500.000000
1442.238430 3190.743320 3.000000 500.000000 500.000000
1408.256886 3332.461807 3.000000 500.000000 500.000000
1374.275341 3474.180294 3.000000 500.000000 500.000000
1340.293797 3615.898780 3.000000 500.000000 500.000000
1306.312252 3757.617267 3.000000 500.000000 500.000000
2474.041690 949.506000 4.000000 500.000000 500.000000
2618.205818 957.743950 4.000000 500.000000 500.000000
2762.369946 965.981900 4.000000 500.000000 500.000000
2906.534074 974.219850 4.000000 500.000000 500.000000
3050.698203 982.457800 4.000000 500.000000 500.000000
3194.862331 990.695751 4.000000 500.000000 500.000000
3339.026459 998.933701 4.000000 500.000000 500.000000
3483.190587 1007.171651 4.000000 500.000000 500.000000
3627.354715 1015.409601 4.000000 500.000000 500.000000
3771.518843 1023.647551 4.000000 500.000000 500.000000
2362.829363 221.992024 5.000000 500.000000 500.000000
2500.918002 271.574187 5.000000 500.000000 500.000000
2639.006642 321.156350 5.000000 500.000000 500.000000
2777.095282 370.738512 5.000000 500.000000 500.000000
2915.183922 420.320675 5.000000 500.000000 500.000000
3053.272562 469.902838 5.000000 500.000000 500.000000
3191.361202 519.485000 5.000000 500.000000 500.000000
3329.449842 569.067163 5.000000 500.000000 500.000000
3467.538482 618.649326 5.000000 500.000000 500.000000
3605.627121 668.231488 5.000000 500.000000 500.000000
3743.715761 717.813651 5.000000 500.000000 500.000000
2163.573943 1412.890697 6.000000 500.000000 500.000000
2199.486257 1554.223030 6.000000 500.000000 500.000000
2235.398571 1695.555363 6.000000 500.000000 500.000000
2271.310885 1836.887695 6.000000 500.000000 500.000000
2307.223199 1978.220028 6.000000 500.000000 500.000000
2343.135513 2119.552361 6.000000 500.000000 500.000000
2379.047827 2260.884694 6.000000 500.000000 500.000000
2414.960141 2402.217027 6.000000 500.000000 500.000000
2450.872455 2543.549359 6.000000 500.000000 500.000000
2321.124740 671.475181 7.000000 500.000000 500.000000
2433.495529 769.172122 7.000000 500.000000 500.000000
2545.866318 866.869062 7.000000 500.000000 500.000000
2658.237107 964.566002 7.000000 500.000000 500.000000
2770.607896 1062.262943 7.000000 500.000000 500.000000
2882.978686 1159.959883 7.000000 500.000000 500.000000
2995.349475 1257.656824 7.000000 500.000000 500.000000
3107.720264 1355.353764 7.000000 500.000000 500.000000
3220.091053 1453.050704 7.000000 500.000000 500.000000
3332.461842 1550.747645 7.000000 500.000000 500.000000
3444.832631 1648.444585 7.000000 500.000000 500.000000
3557.203421 1746.141526 7.000000 500.000000 500.000000
3669.574210 1843.838466 7.000000 500.000000 500.000000
data_cryolo_include
loop_
_slice_index #1
I changed the format, keeping only the X and Y coordinates of the box and the size of the box:
1398.989192 917.0690710000001 500.0 500.0
1281.212248 838.293672 500.0 500.0
1163.4353039999999 759.518274 500.0 500.0
1045.65836 680.742875 500.0 500.0
927.8814160000001 601.967476 500.0 500.0
810.104472 523.192078 500.0 500.0
692.327528 444.416679 500.0 500.0
574.550584 365.641281 500.0 500.0
456.77364000000006 286.865882 500.0 500.0
338.996696 208.090484 500.0 500.0
221.219752 129.315085 500.0 500.0
103.442808 50.539685999999996 500.0 500.0
-14.334135999999999 -28.235712 500.0 500.0
1134.8599140000001 1459.229167 500.0 500.0
1013.916508 1546.808875 500.0 500.0
892.9731019999999 1634.388583 500.0 500.0
772.029696 1721.9682899999998 500.0 500.0
651.0862900000001 1809.547998 500.0 500.0
530.142884 1897.1277059999998 500.0 500.0
409.199478 1984.707414 500.0 500.0
288.256072 2072.2871219999997 500.0 500.0
167.31266599999998 2159.86683 500.0 500.0
46.369259 2247.4465379999997 500.0 500.0
-74.574147 2335.026245 500.0 500.0
120.047426 889.2659890000001 500.0 500.0
-56.038759 1093.155256 500.0 500.0
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With the new format, cryolo train can work.
Hello! I use crYOLO for filament picking. I have chosen "Activate filament mode" and the crYOLO model. Although there were no errors when I was running predictions, the log indicates that I can't use the directional method "PREDICTED":
########## The directional method "PREDICTED" can't be used as your model is not a filament model.You need to retrain your picking model. Fall back to old directional method "CONVOLUTION". ##########
How to use directional method "PREDICTED"? Also I want to konw weather PREDICTED is more precise than CONVOLUTION ?
By the way, tutorials document of Install napari and the boxmanager plugin is incorrect. I cannot install napari by conda while the command
pip install napari
can work. :)