Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
Resources  
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Explore Pricing
Product  
Data Engine LLMs Platform Enterprise
Connect to Dagshub Discord channel
Register
Login

cwerner
/
deadtrees
connected to https://github.com/cwerner/deadtrees.git

Watch 2
Issues 14 Pull Requests 2 Discussions 0 Invite collaborators

dvc.lock 6.5 KB
Permalink History Raw

You have to be logged in to leave a comment. Sign In
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185 
  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   createtiles@2019:
    4. 

  4.     cmd: mkdir -p data/processed.images.2019; gdal_retile.py  -csv locations.csv  -v
    5. 

  5.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    6. 

  6.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2019  data/raw/ortho_ms_2019_EPSG3044.tif
    7. 

  7.     deps:
    8. 

  8.     - path: data/raw/ortho_ms_2019_EPSG3044.tif
    9. 

  9.       md5: 28a27ea53b559c3cec937a3687407557
    10. 

  10.       size: 144920418531
    11. 

  11.     params:
    12. 

  12.       params.yaml:
    13. 

  13.         source_dim: 2048
    14. 

  14.     outs:
    15. 

  15.     - path: data/processed.images.2019
    16. 

  16.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    17. 

  17.       size: 144926802415
    18. 

  18.       nfiles: 30489
    19. 

  19.   createmasks:
    20. 

  20.     cmd: python scripts/createmasks.py  data/processed.images.2019  data/processed.masks.2019  data/raw/shapefiles/deadtrees_2019/deadtrees_2019.shp
    21. 

  21.       --negativesample data/raw/shapefiles/deadtrees_notdead/deadtrees_notdead.shp
    22. 

  22.     deps:
    23. 

  23.     - path: data/processed.images.2019
    24. 

  24.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    25. 

  25.       size: 144926802415
    26. 

  26.       nfiles: 30489
    27. 

  27.     - path: data/raw/shapefiles/deadtrees_2019
    28. 

  28.       md5: 206432f450611c44ceb32c49e50aa8c9.dir
    29. 

  29.       size: 3996517
    30. 

  30.       nfiles: 5
    31. 

  31.     - path: data/raw/shapefiles/deadtrees_notdead
    32. 

  32.       md5: 99b6d382aebfc46e79c9d044e95add4e.dir
    33. 

  33.       size: 31722
    34. 

  34.       nfiles: 5
    35. 

  35.     outs:
    36. 

  36.     - path: data/processed.masks.2019
    37. 

  37.       md5: 86dfe9fff1765527e6ab7bdbd50e873d.dir
    38. 

  38.       size: 4103103156
    39. 

  39.       nfiles: 978
    40. 

  40.     - path: data/processed.masks.2019.neg_sample
    41. 

  41.       md5: e39614f069603f65a333d0a8713072a9.dir
    42. 

  42.       size: 335615520
    43. 

  43.       nfiles: 80
    44. 

  44.   createdataset:
    45. 

  45.     cmd: python scripts/createdataset.py  data/processed.images.2019  data/processed.masks.2019  data/dataset  --source_dim
    46. 

  46.       2048 --tile_size 256 --format TIFF
    47. 

  47.     deps:
    48. 

  48.     - path: data/processed.images.2019
    49. 

  49.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    50. 

  50.       size: 144926802415
    51. 

  51.       nfiles: 30489
    52. 

  52.     - path: data/processed.masks.2019
    53. 

  53.       md5: 86dfe9fff1765527e6ab7bdbd50e873d.dir
    54. 

  54.       size: 4103103156
    55. 

  55.       nfiles: 978
    56. 

  56.     - path: data/processed.masks.2019.neg_sample
    57. 

  57.       md5: e39614f069603f65a333d0a8713072a9.dir
    58. 

  58.       size: 335615520
    59. 

  59.       nfiles: 80
    60. 

  60.     params:
    61. 

  61.       params.yaml:
    62. 

  62.         createdataset.tile_size: 256
    63. 

  63.         file_type: TIFF
    64. 

  64.         source_dim: 2048
    65. 

  65.     outs:
    66. 

  66.     - path: data/dataset/stats.csv
    67. 

  67.       md5: 5d5e6ab2648bdb6b7846d4c8655a72a8
    68. 

  68.       size: 2539255
    69. 

  69.     - path: data/dataset/train
    70. 

  70.       md5: 155ce2ddabeab3e619b59e016093ae30.dir
    71. 

  71.       size: 6895349760
    72. 

  72.       nfiles: 162
    73. 

  73.   createbalanced:
    74. 

  74.     cmd: python scripts/createbalanced.py  data/dataset/stats.csv data/dataset/train  data/dataset/train_balanced  data/dataset/train_balanced_short
    75. 

  75.       --format TIFF --tmp-dir ./tmp
    76. 

  76.     deps:
    77. 

  77.     - path: data/dataset/stats.csv
    78. 

  78.       md5: 8f24c9c9fcbcab4f9388a4b2b26ed58c
    79. 

  79.       size: 2415683
    80. 

  80.     - path: data/dataset/train
    81. 

  81.       md5: a5a9bd6897f3c8b0143e3a378928cc54.dir
    82. 

  82.       size: 20627302400
    83. 

  83.       nfiles: 121
    84. 

  84.     params:
    85. 

  85.       params.yaml:
    86. 

  86.         file_type: TIFF
    87. 

  87.     outs:
    88. 

  88.     - path: data/dataset/train_balanced
    89. 

  89.       md5: 00c0e3636b55b92b63add488a2e62a12.dir
    90. 

  90.       size: 20596592640
    91. 

  91.       nfiles: 241
    92. 

  92.     - path: data/dataset/train_balanced_short
    93. 

  93.       md5: 94e764aa3326c386524910bfbac0454d.dir
    94. 

  94.       size: 2072975360
    95. 

  95.       nfiles: 97
    96. 

  96.   createtiles@2017:
    97. 

  97.     cmd: mkdir -p data/processed.images.2017; gdal_retile.py  -csv locations.csv  -v
    98. 

  98.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    99. 

  99.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2017  data/raw/ortho_ms_2017_EPSG3044.tif
    100. 

  100.     deps:
    101. 

  101.     - path: data/raw/ortho_ms_2017_EPSG3044.tif
    102. 

  102.       md5: 2389bdb4952d2c869c52e87abce30d4f
    103. 

  103.       size: 109896357004
    104. 

  104.     params:
    105. 

  105.       params.yaml:
    106. 

  106.         source_dim: 2048
    107. 

  107.     outs:
    108. 

  108.     - path: data/processed.images.2017
    109. 

  109.       md5: 66de96d201af5a47a09997691b992370.dir
    110. 

  110.       size: 109902740888
    111. 

  111.       nfiles: 30489
    112. 

  112.   inference@2017:
    113. 

  113.     cmd: mkdir -p data/predicted.2017; mkdir -p data/predicted.2017_preview; stdbuf
    114. 

  114.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2017
    115. 

  115.       data/processed.images.2017; gdal_merge.py  -co 'COMPRESS=LZW'  -co 'TILED=YES'  -o
    116. 

  116.       data/predicted_mosaic_2017.tif  data/predicted.2017/ortho_ms_2017_EPSG3044_*
    117. 

  117.     deps:
    118. 

  118.     - path: data/processed.images.2017
    119. 

  119.       md5: 66de96d201af5a47a09997691b992370.dir
    120. 

  120.       size: 109902740888
    121. 

  121.       nfiles: 30489
    122. 

  122.     outs:
    123. 

  123.     - path: data/predicted.2017
    124. 

  124.       md5: 5f6c51487c2ea4ffff3430d44e694799.dir
    125. 

  125.       size: 599348567
    126. 

  126.       nfiles: 20827
    127. 

  127.     - path: data/predicted.2017_preview
    128. 

  128.       md5: 9410905292c82df9389999349a157931.dir
    129. 

  129.       size: 87265506654
    130. 

  130.       nfiles: 20827
    131. 

  131.     - path: data/predicted_mosaic_2017.tif
    132. 

  132.       md5: 9f920c44fb88f61dd77ef8ff47eeed5d
    133. 

  133.       size: 834945563
    134. 

  134.   inference@2019:
    135. 

  135.     cmd: mkdir -p data/predicted.2019; mkdir -p data/predicted.2019_preview; stdbuf
    136. 

  136.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2019
    137. 

  137.       data/processed.images.2019; gdal_merge.py  -co 'COMPRESS=LZW'  -co 'TILED=YES'  -o
    138. 

  138.       data/predicted_mosaic_2019.tif  data/predicted.2019/ortho_ms_2019_EPSG3044_*
    139. 

  139.     deps:
    140. 

  140.     - path: data/processed.images.2019
    141. 

  141.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    142. 

  142.       size: 144926802415
    143. 

  143.       nfiles: 30489
    144. 

  144.     outs:
    145. 

  145.     - path: data/predicted.2019
    146. 

  146.       md5: 7fe1bc28db7505e7db29ee31872ee0a2.dir
    147. 

  147.       size: 480433774
    148. 

  148.       nfiles: 16227
    149. 

  149.     - path: data/predicted.2019_preview
    150. 

  150.       md5: 1fb8d486945228d9aad87d6616512247.dir
    151. 

  151.       size: 68062950702
    152. 

  152.       nfiles: 16227
    153. 

  153.     - path: data/predicted_mosaic_2019.tif
    154. 

  154.       md5: 591fd24b8603981246a1967edef791ab
    155. 

  155.       size: 803971970
    156. 

  156.   computestats:
    157. 

  157.     cmd: 'python scripts/computestats.py --frac 0.1 data/processed.images.2017   data/processed.images.2019 '
    158. 

  158.     deps:
    159. 

  159.     - path: data/processed.images.2017
    160. 

  160.       md5: 66de96d201af5a47a09997691b992370.dir
    161. 

  161.       size: 109902740888
    162. 

  162.       nfiles: 30489
    163. 

  163.     - path: data/processed.images.2019
    164. 

  164.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    165. 

  165.       size: 144926802415
    166. 

  166.       nfiles: 30489
    167. 

  167.     outs:
    168. 

  168.     - path: data/processed.images.stats.json
    169. 

  169.       md5: 453f256a6c2bf5359cf3e85b420a54e0
    170. 

  170.       size: 585
    171. 

  171.   computestatsinference:
    172. 

  172.     cmd: 'python scripts/computestats_inference.py data/predicted.2017   data/predicted.2019 '
    173. 

  173.     deps:
    174. 

  174.     - path: data/predicted.2017
    175. 

  175.       md5: 5f6c51487c2ea4ffff3430d44e694799.dir
    176. 

  176.       size: 599348567
    177. 

  177.       nfiles: 20827
    178. 

  178.     - path: data/predicted.2019
    179. 

  179.       md5: 7fe1bc28db7505e7db29ee31872ee0a2.dir
    180. 

  180.       size: 480433774
    181. 

  181.       nfiles: 16227
    182. 

  182.     outs:
    183. 

  183.     - path: data/predicted.stats.csv
    184. 

  184.       md5: 6d86b56ec9f16c4a87f637e03efd162c
    185. 

  185.       size: 1323925
    186.
Prev
Next
Tip!

Press p or to see the previous file or, n or to see the next file

Comments

Write Preview
Loading...
Integrate Google Cloud Storage
Use Google Storage
Select bucket
Upload key
Finish

Use Google Cloud Storage!

Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Google Storage bucket

Service Account Key

Congratulations!

deadtrees is now integrated with Google Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate AWS S3
Use S3 remote
Select bucket
Access key
Finish

Use AWS S3 as storage!

Browsing data directories saved to S3 is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

deadtrees is now integrated with AWS S3!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate S3 compatible storage
Use S3 like remote
Select bucket
Access key
Finish

Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Existing keys:
s3://deadtrees/dvc
Apr 28, 2022

Specify your S3 bucket

Access key (If needed)

Congratulations!

deadtrees is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
Finish

Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

deadtrees is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes