Do you know another Face Landmark dataset that is in the same format? aka 68 landmarks in csv format for the groundtruth? I am looking for something with at least 1000 data point and this one only has 69 data points. For example, ibug dataset has a .pt for each image not an integrated .csv groundtruth

  
      github.com
  
  
    Gogumee/warp/blob/master/face_landmarks.csv
image_name,part_0_x,part_0_y,part_1_x,part_1_y,part_2_x,part_2_y,part_3_x,part_3_y,part_4_x,part_4_y,part_5_x,part_5_y,part_6_x,part_6_y,part_7_x,part_7_y,part_8_x,part_8_y,part_9_x,part_9_y,part_10_x,part_10_y,part_11_x,part_11_y,part_12_x,part_12_y,part_13_x,part_13_y,part_14_x,part_14_y,part_15_x,part_15_y,part_16_x,part_16_y,part_17_x,part_17_y,part_18_x,part_18_y,part_19_x,part_19_y,part_20_x,part_20_y,part_21_x,part_21_y,part_22_x,part_22_y,part_23_x,part_23_y,part_24_x,part_24_y,part_25_x,part_25_y,part_26_x,part_26_y,part_27_x,part_27_y,part_28_x,part_28_y,part_29_x,part_29_y,part_30_x,part_30_y,part_31_x,part_31_y,part_32_x,part_32_y,part_33_x,part_33_y,part_34_x,part_34_y,part_35_x,part_35_y,part_36_x,part_36_y,part_37_x,part_37_y,part_38_x,part_38_y,part_39_x,part_39_y,part_40_x,part_40_y,part_41_x,part_41_y,part_42_x,part_42_y,part_43_x,part_43_y,part_44_x,part_44_y,part_45_x,part_45_y,part_46_x,part_46_y,part_47_x,part_47_y,part_48_x,part_48_y,part_49_x,part_49_y,part_50_x,part_50_y,part_51_x,part_51_y,part_52_x,part_52_y,part_53_x,part_53_y,part_54_x,part_54_y,part_55_x,part_55_y,part_56_x,part_56_y,part_57_x,part_57_y,part_58_x,part_58_y,part_59_x,part_59_y,part_60_x,part_60_y,part_61_x,part_61_y,part_62_x,part_62_y,part_63_x,part_63_y,part_64_x,part_64_y,part_65_x,part_65_y,part_66_x,part_66_y,part_67_x,part_67_y
0805personali01.jpg,27,83,27,98,29,113,33,127,39,139,49,150,60,159,73,166,87,168,100,166,111,160,120,151,128,141,133,128,137,116,138,102,138,89,44,70,53,66,63,65,73,66,82,70,103,72,111,69,120,68,128,69,135,73,92,82,92,91,92,100,92,109,82,117,86,119,91,120,95,119,99,118,55,82,62,78,70,79,76,84,69,86,61,85,106,86,111,81,119,81,125,85,120,88,112,88,71,137,78,131,85,127,90,129,94,129,99,134,103,142,97,144,92,145,88,145,83,144,77,141,75,137,85,134,89,135,93,136,100,141,93,135,89,135,84,134
1084239450_e76e00b7e7.jpg,70,236,71,257,75,278,82,299,90,320,100,340,111,359,126,375,149,379,175,376,197,364,218,346,236,322,249,296,254,266,256,237,256,207,65,210,69,197,80,193,93,196,106,201,143,196,160,185,179,180,198,182,212,193,127,212,126,230,126,248,125,266,113,279,122,283,132,285,143,281,154,277,81,224,87,216,98,215,110,222,99,225,88,227,159,216,170,207,181,206,192,212,183,215,171,216,110,314,117,310,126,308,135,309,147,307,164,306,184,307,167,317,152,321,139,323,130,322,120,318,114,313,127,312,136,313,148,311,179,308,149,312,137,314,128,312
10comm-decarlo.jpg,66,114,65,128,67,142,68,156,72,169,80,180,91,189,103,195,115,197,128,197,139,192,148,184,154,173,160,162,164,150,169,138,171,126,80,101,87,95,98,92,108,96,116,101,139,103,147,100,156,100,164,105,168,113,127,113,127,121,126,128,126,136,115,143,119,145,124,147,128,146,132,146,90,112,97,108,104,109,110,114,103,115,96,114,139,118,146,114,153,116,157,121,152,122,146,121,101,163,109,157,117,155,123,157,129,156,134,160,139,168,133,171,126,172,120,172,114,171,107,168,104,162,116,161,122,162,128,162,136,167,127,166,121,165,116,164
110276240_bec305da91.jpg,42,140,45,161,51,180,61,200,73,220,89,238,108,255,129,268,155,268,180,261,203,244,222,224,238,199,247,172,248,144,245,116,241,87,39,114,45,102,57,97,72,96,87,99,120,91,136,78,155,70,178,66,199,71,108,114,111,132,115,150,118,168,107,180,116,182,127,182,138,177,147,171,57,134,65,127,77,122,92,125,80,130,68,134,143,111,155,103,167,100,182,101,170,107,158,109,98,207,107,199,120,193,131,193,142,188,162,185,184,187,168,206,151,218,139,222,127,223,113,219,103,206,122,203,133,201,144,197,180,189,147,204,136,208,125,209
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137341995_e7c48e9a75.jpg,-4,129,1,178,10,231,30,276,59,314,98,349,135,375,171,398,205,402,242,387,284,358,327,327,366,290,390,240,398,185,394,126,391,66,10,77,23,45,52,23,92,14,130,21,180,13,209,-5,248,-6,284,2,317,19,157,67,158,93,160,121,161,150,129,184,152,188,176,190,197,182,216,170,47,102,65,81,91,78,123,91,95,100,69,105,211,72,232,54,258,48,283,58,263,70,237,74,119,265,134,240,156,224,178,226,199,216,237,224,271,238,249,289,218,311,194,316,170,318,144,303,130,265,161,244,182,241,204,238,260,241,210,281,188,286,166,286
1383023626_8a49e4879a.jpg,0,48,-6,94,-10,141,-8,186,6,228,26,265,49,299,78,325,110,334,140,328,166,303,191,276,218,248,245,219,265,187,278,152,287,114,55,64,84,58,113,61,141,70,164,85,216,90,236,79,258,75,278,75,291,84,185,113,182,145,180,176,176,208,127,198,145,210,164,221,185,214,200,203,80,96,101,99,119,101,135,107,116,107,98,104,214,112,233,110,249,111,263,112,248,117,232,116,70,216,104,222,135,226,152,231,168,228,185,

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      pytorch.org
  
  
    

Developing Custom PyTorch Dataloaders — PyTorch Tutorials 1.6.0 documentation 1



  
  
    
    
  
  


(base) mona@mona:~/research/facial_landmark$ ls ibug_300W_large_face_landmark_dataset
total 43M
drwxrwxr-x 4 mona mona 4.0K Aug  9  2014 helen
drwxrwxr-x 4 mona mona 4.0K Aug  9  2014 lfpw
drwxrwxr-x 2 mona mona  40K Aug 16  2014 afw
drwxrwxr-x 2 mona mona  20K Aug 16  2014 ibug
-rw-rw-r-- 1 mona mona 4.0K Aug 16  2014 image_metadata_stylesheet.xsl
-rw-rw-r-- 1 mona mona  21M Aug 16  2014 labels_ibug_300W.xml
-rw-rw-r-- 1 mona mona  19M Aug 16  2014 labels_ibug_300W_train.xml
-rw-rw-r-- 1 mona mona 2.8M Aug 16  2014 labels_ibug_300W_test.xml
drwxrwxr-x 6 mona mona 4.0K Jul 24  2015 .
drwxrwxr-x 6 mona mona 4.0K Oct  6 21:28 ..