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162 values
Simon-ARC-RLE-Image, translate x by +1
2 17 9,,96,94,,,,,,4,,94,,9,,,
2 17 9,,69,49,,,,,,4,,49,,9,,,
dataset=image_deserialize group=translate_x_plus1 image_width=small image_height=medium
With SimonsRLEImage, all pixels inside 3x3 have same color as center
17 14 61621a26a262a69a2,926a26d2b62a6,169a2925b2b6191,b2b625a2626a219,629626512a626a212,b292951b26c12,a26a265a61a26a292,b2b95616261a6a2,a6b25921a6a1b61,1b26521a2626c2,a1a262126212b161,d2b6269a26121,262191a62a621b21,c262196a26a29a2
17 14 0,,,,,,,,,,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=medium image_height=medium
process SIMONSARCRLEIMAGE and return the transposed
7 16 b575a7,7,,75d7,7a595a7,b769a7,a7569a7,a576797,b76797,a576570,b76a70,5a76a70,5a76570,b76a70,b365a7,7
16 7 5e75757a5737,5a7a5a7575c737,5b7575f737,c79i67,5b75a9a75a75757,f7a9f7,h7d0a7
dataset=image_deserialize group=transpose image_width=small image_height=medium
Rotate counterclockwise simons-arc-image
17 17 d0a606010a6b0,0a64060b61060a60,0a404a6b0106b06,606a06b017a060a4,b0a6b06176a0606,4d060617a606a0,8640a6b017a06a06,68c060617a06060,a68a06a01f06,6086a0a6106a06a05,b08b06d10695,606084a010a5a6950,0a6068061b0a5056,606a08016b090560,606b081a069a6b0,606c0806a0b6a0,6a04a60b6040a6a0
17 17 a06460606a506c0,0604b06a09a56b0,06a0a6c069050a6,6a06a0a6060650b6,b6a06c01659a60,c0a6c015a0904,b1d70615a06a0,060d1a01c0a6,a6a0a606d16a06,06f0a606a186,606a060606c08a0,c6a0606a04a8a06,a040606c086b06,04a06c068d04,0646a040a80d60,064b0686b06c0,b06048b6060c6
dataset=image_deserialize group=rotate_ccw image_width=medium image_height=medium
convert SimonsRLEImage and return the transposed
2 4 6,15,16,69
4 2 6a16,6569
dataset=image_deserialize group=transpose image_width=small image_height=small
Simons-ARC-RLE-Image, where 3 neighbors have the same color as the center pixel
14 7 70b7395c757,d737a9a70a7,05b73757a9a70,a7a503a7a57097,757573075b790,b7b9705a7597,7a9a5a0a7570a7
14 7 c01e01a0,l01,c0101f0,e0101d0,a01c0a1a01a0,1l0,0
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=small
SIMONARCRLEIMAGE, move up by 1 pixel
21 12 h41c41e4,d41g41e4,m40a41b4,4a141a41e40e4,1d41f40e4,m40e4,i4a1a40e4,41i410f4,l40e41,k41041b414,d4a1c41404a1c4,n41d4
21 12 d41g41e4,m40a41b4,4a141a41e40e4,1d41f40e4,m40e4,i4a1a40e4,41i410f4,l40e41,k41041b414,d4a1c41404a1c4,n41d4,h41c41e4
dataset=image_deserialize group=translate_y_minus1 image_width=large image_height=medium
Rotate 180 SIMONARCIMAGE
18 15 a48d45845b6454,4848648c6a4b645,54686a46c46d4,a48a4a564648b6b4,486a4846a8c6c4,68a4a646a4a8a46a46,646486b4648a68a64,654646a8b4a646486,6a4a864a6a4a68a4a8,b6a464184645a48a4,a8a46a414a648b484,486c416a4a648a64,8468481a648b6c4,8a4a5a6a4648a46a48,6464684b6b468a48
18 15 8a486b4b64864646,8a46a4846a4a6a5a48,c4b684a61848648,4a684a6a461c4684,48b484a641a46a4a8,a48a454648146a4b6,a8a48a6a4a646a8a46,68464a6b4a8646456,4a68a6846b4684646,6a46a4a8a464a6a486,c4c6a8648a4684,b4b684646a5a48a4,d46c46a468645,54b6a4c68468484,454b65485d48a4
dataset=image_deserialize group=rotate_180 image_width=medium image_height=medium
SIMONSARCRLEIMAGE, move right by 1 pixel
4 21 4,,a424,4924,a424,,42a4,32a4,,,,,,03a4,,,,,43a4,4,
4 21 4,,b42,a492,b42,,a424,4324,,,,,,4034,,,,,a434,4,
dataset=image_deserialize group=translate_x_plus1 image_width=small image_height=large
Simon-ARC-RLE-Image, where 3 neighbors have the same color as the center pixel
13 21 18a171817c1,18c178c18,8a171b787b1,b181a818a189,f1a8b19,a187e1795,8a1a81a8b195,76e171951,76e178951,186a18a189157,786b18a19157,a16a187191851,171681819c1,1a868a19d1,7a186a1917a87,8a18619a18a18,b17679718717,a8a186b1b87,18a186a8a1a81,81878178a1b8,8b1a81a7a187
13 21 a01i0,d0a101c0,e01e0,g01c0,f01d0,0,i01a0,h01b0,0,,,01b0101d0,1c0101d0,k01,a0a1h0,g01c0,h01b0,h01010,b01h0,c01f01,a01e01010
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=large
Process SimonsArcRleImage and return the transposed
7 17 a987a98,987b98,a8908a9,9780891,a909701,890a917,a9080a1,90a9191,9089181,0a91784,3791984,9318a94,a819847,9193949,91a9349,8b9849,b98a98
17 7 a98a98b90398a989,9a87b9a09738a1a9,8798b098a9a1c9,79a0a98a9a1893a98,a9a8790a17a989389,c90a19b89c49,a89a17b1b47b98
dataset=image_deserialize group=transpose image_width=small image_height=medium
With SimonsRLEImage, 3x3 area, where all pixels have same color as center
1 10 9,,,,,,5,7,,9
1 10 0,,,,,,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=small image_height=small
Compress X and Y SIMONARCIMAGE
16 9 92a5a253a89a2a59,c5323a52b8959,9a52a3a2925a9595,29a5a3925929b52,5259a3252d5a2,5a83f85a652,a585a3529a2a5259,5a2a59a59b25259,529a52a592525295
16 9 92a5a253a89a2a59,c5323a52b8959,9a52a3a2925a9595,29a5a3925929b52,5259a3252d5a2,5a83f85a652,a585a3529a2a5259,5a2a59a59b25259,529a52a592525295
dataset=image_deserialize group=compress_xy image_width=medium image_height=small
move down by 1 pixel SIMONSARCIMAGE
18 3 a9a6b868b5b8a09,e7g2b68,e5e79868a9
18 3 e5e79868a9,a9a6b868b5b8a09,e7g2b68
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=small
Half rotate SIMONARCIMAGE
21 13 d51e5151561b5,b51b565a1a5651c51,1515a1a52151a5a6a1651,1561a5a75a256585b1a6,5a61a5a6c7a5985a651,51c5151564a74851b5,51d5a1d52a41b5,d5a8c35a169261a5,a515161a8a5d393b1,b5651b5b8a56195651,56516a561b5a85195161,c516a15a15a6a819515,1b56b5a15156a5895a1
21 13 a1598a56515a1b56b51,51591a8a65a15a161c5,1615915a8b516a561565,1565916a5b8b5156b5,b139d3a5a816151a5,a516296a15c3a8d5,b51a42d5a1d515,b51584a7465151c515,15a6589a5c7a6a51a65,a6b158565a25a7a51651,156a1a6a51512a5a15151,1c5156a5a156b51b5,b5165151e51d5
dataset=image_deserialize group=rotate_180 image_width=large image_height=medium
SimonsRLEImage, move right by 1 pixel
20 19 c61h61a6161,1b61e6161e6,61a61a616a1g76,6a19f716d161,6a1961b61a61a6a1b6,1619a1b6c1a6b1a6,b69a1a61a6c16b16,a619a61a6161616c96,6169a16g9d6,b1c9c1a6161a616,6a146a1a61g6a1,619461a61b616161616,169461a61a6c1a6a16,a19416b1b6b16a1a6,a64c1a6a161f6,614c1b616161b616,a646b16b1a6161c6,1646a1a616a1b6a1b6,6146a16d1616c16
20 19 1c61h61a616,61b61e6161d6,a61a61a616a1g7,16a19f716d16,a6a1961b61a61a6a1a6,61619a1b6c1a6b16,c69a1a61a6c16b1,b619a61a6161616c9,a6169a16g9c6,6b1c9c1a6161a61,16a146a1a61g61,a619461a61b61616161,6169461a61a6c1a6a1,6a19416b1b6b16a16,b64c1a6a161e6,a614c1b616161b61,b646b16b1a6161b6,61646a1a616a1b6a1a6,a6146a16d1616c1
dataset=image_deserialize group=translate_x_plus1 image_width=medium image_height=medium
compress x SIMONARCIMAGE
3 14 5a1,951,9a5,951,795,,,791,795,759,519,a54,541,1
3 14 5a1,951,9a5,951,795,,,791,795,759,519,a54,541,1
dataset=image_deserialize group=compress_x image_width=small image_height=medium
compress x SimonsArcRleImage
9 2 e4a64,c4a6b4
4 2 a464,46a4
dataset=image_deserialize group=compress_x image_width=small image_height=small
Rotate clockwise Simon-ARC-RLE-Image
9 2 9b860595,c3c43
2 9 39,38,,,46,40,45,49,35
dataset=image_deserialize group=rotate_cw image_width=small image_height=small
Simons-ARC-Image, translate y by +1
17 15 1,,,,,,,,,,,,,,
17 15 1,,,,,,,,,,,,,,
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=medium
simons-arc-image compress xy
15 2 c03a63c60a6,a63i6a3
9 2 b0363606,63e63
dataset=image_deserialize group=compress_xy image_width=medium image_height=small
move down by 1 pixel Simon-ARC-RLE-Image
11 2 9a32c9b2,2a9a292c9
11 2 2a9a292c9,9a32c9b2
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=small
convert SIMONSARCIMAGE and return the flipx
7 13 a2d1,7041701,1410a10,14b101,7b10a1,717b17,7a170a1,7a10170,a7b107,70d1,71710a1,7d10,07b1a7
7 13 d1a2,1071407,0a10141,10b141,a10b17,7b1717,a107a17,0710a17,70b1a7,d107,a101717,0d17,a7b170
dataset=image_deserialize group=flipx image_width=small image_height=medium
Rotate clockwise SIMONSARCIMAGE
21 13 j4d303b0,a3b0b3a4d3b0a30,30b3034a30b303030a3,30303a40b3c03b030,30a34a3c0b3b030a3,a34c0c303b0a3b0,c03b0a30d3a0b3,b0a30b3a0b3a0d3,30f3e03a0a30,303c0a30a3b030a303,a3b0a3a0a3d0303a0,0b303a03a0303b030a3,a03b030a3b03a0a3030
13 21 a0b3a0d34,0a3c03b034,a30a3a04b304,03a0a3a030304,c0b304a304,0a303b034034,3030a3a034a34,b0b303a0434,a30d30a3a4,303b0a30a3a4,a0a3b0303034,03030a3030b3,d0c30b3,a3b0a3030b3,e03c0a3,b0a303a0a303,303a0303d0,a303030a30303,a0d3c030,a3a0b30c30,03030a30303a0
dataset=image_deserialize group=rotate_cw image_width=large image_height=medium
Rotate Clockwise Simon-ARC-RLE-Image
10 1 a38b6c2
1 10 3,,8,6,,,2,,,
dataset=image_deserialize group=rotate_cw image_width=small image_height=small
SIMONSARCIMAGE, 3x3 area, locations where all neighbors have the same color as center
2 7 74,79,47,32,,3,23
2 7 0,,,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=small image_height=small
FlipY SIMONARCIMAGE
4 16 a585,5,2152,5805,5205,a502,a202,5a81,1a51,5,5a25,5215,b58,a525,b52,2a51
4 16 2a51,b52,a525,b58,5215,5a25,5,1a51,5a81,a202,a502,5205,5805,2152,5,a585
dataset=image_deserialize group=flipy image_width=small image_height=medium
SimonsRLEImage, where 3 of the 3x3 neighbors have the same color as the center pixel
13 6 21721247a9b1,4d041a7a19,a1a0242b0121,2717147e2,9a214b127291,191b2a17b12
13 6 i01a0,c01g0,j010,f0a1c0,b01010a101a0,f01d0
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=small
simons-arc-image, return translated x+1
15 2 a1c0a2b4b50,0a1a0i7
15 2 0a1c0a2b4b5,70a1a0h7
dataset=image_deserialize group=translate_x_plus1 image_width=medium image_height=small
move down by 1 pixel SIMONSARCIMAGE
19 13 f85f85858,58a5g858a51a8,b85g85851b8,a85b8a5b85a81c8,c85d8a58185a85,5d858585815858a5,8a5a85b85a8185c8,j81a85a858,5h81g8,a6b858581f858,28c67818585e8,2a8a7565e85c8,8a185m8
19 13 8a185m8,f85f85858,58a5g858a51a8,b85g85851b8,a85b8a5b85a81c8,c85d8a58185a85,5d858585815858a5,8a5a85b85a8185c8,j81a85a858,5h81g8,a6b858581f858,28c67818585e8,2a8a7565e85c8
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=medium
simons-arc-image, where 6 of the 3x3 neighbors have the same color as the center pixel
15 7 84a7a4a2424c2,480742a42a424a2,24a80f4274,d3a4a2a42b4,2a4a1c34a2a42,7c4a247247b4,724a247a4a274a2
15 7 0,h01d0,e01a0a1c0,0,,,
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=small
Simons-ARC-RLE-Image Compress-Y
12 5 a2c5a72b7,e0e5,b7b3e0,b74d7b3,7
12 5 a2c5a72b7,e0e5,b7b3e0,b74d7b3,7
dataset=image_deserialize group=compress_y image_width=medium image_height=small
convert SIMONARCIMAGE and return the transposed
9 17 4b5a6565,b5a6b54,4a54b565,6f56,6b5b6a5,b56c56,54650b65,5645108a5,6b5107a5,c5150a5,656157605,545157505,b5178a50,457174565,4a51b545,f545,a54e5
17 9 454a6b5656a5a4a5,e546b54d5,e564a56a57a54,564a56c5d1a5,a6a5650b1a5a7b5,6b5656a05a784b5,c56568706e5,656b56b5a056a45,545656e50c5
dataset=image_deserialize group=transpose image_width=small image_height=medium
Compress Y SIMONARCIMAGE
12 14 e60b650,a670a676a576,0a60c65b6,a6507a60a676,607606065096,605a65c696,650b6060596,a687c60969,0a6a87a695a7,65a618a76565,0c618a65a6,b69161a8b6,06a1b615606,01a560605067
12 14 e60b650,a670a676a576,0a60c65b6,a6507a60a676,607606065096,605a65c696,650b6060596,a687c60969,0a6a87a695a7,65a618a76565,0c618a65a6,b69161a8b6,06a1b615606,01a560605067
dataset=image_deserialize group=compress_y image_width=medium image_height=medium
Compress-XY SIMONSARCIMAGE
1 7 4,9,,,,,
1 2 4,9
dataset=image_deserialize group=compress_xy image_width=small image_height=small
Get column 5 as pixels, SIMONSARCIMAGE
8 4 69a2c7,3c0b3,a75a3b7,b75c7
7377
dataset=image_deserialize group=get_column_as_list image_width=small image_height=small
Simon-ARC-RLE-Image, Get pixels from column 6
12 15 a3536a3b6a3,a6563936b36,b359b3b63,b6593c6a3,6a39563a63a6,b396d3a6,3696e3a6,369b6a36b3,696363c6a3,6963b6363a6,963b6a36363,b3a63636b3,a6b36a3c6,a63c6a3b6,363636c3a6
333633336636363
dataset=image_deserialize group=get_column_as_list image_width=medium image_height=medium
compress xy Simons-ARC-Image
17 11 b51c51b591951,1c51a8959a51591,95a91535b8a51595,a9a5153a419a8b51,91a95a93515158415,5b9515319a5a4a15,515b91315a41a519,151c5347a59a595,51a5a14a6a7159159,95954a6951a719a15,b9a6c51a579515
17 11 b51c51b591951,1c51a8959a51591,95a91535b8a51595,a9a5153a419a8b51,91a95a93515158415,5b9515319a5a4a15,515b91315a41a519,151c5347a59a595,51a5a14a6a7159159,95954a6951a719a15,b9a6c51a579515
dataset=image_deserialize group=compress_xy image_width=medium image_height=medium
With SIMONARCRLEIMAGE, where 3 neighbors have the same color as the center pixel
14 13 3a45a2a42c54,54524235a24352,425a2453432b3,4a24a235a25354,43a5a45a2d5,24a2345b23242,b23453c25a2,52b54a52a3425,523525252a3232,a54525a2a65a25,b5245a2425253,32325a2a53a432,3a53a545b25a3
14 13 c01e01a0,010101b01c0,j01a0,a0101b0b1010,c01d01010,a01a01g0,1c01c01a01,b01d0a1b0,1c01b0b1a0,j01a0,101j0,c0101d010,0
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=medium
SimonsArcRleImage, return translated x+1
13 10 c0c27a230,g0873a0,g037b0,96c0a3087a0,7a90a3c0870,7a09e08a0,7a309e060,7a060a96d0,a6c06e0,0
13 10 d0c27a23,h08730,h037a0,096c0a30870,07a90a3c087,07a09e080,07a309e06,07a060a96c0,0a6c06d0,0
dataset=image_deserialize group=translate_x_plus1 image_width=medium image_height=small
SIMONSARCRLEIMAGE, return translated y+1
15 10 a84c804a84b8,a8707a8474878a7,8a40a4a87807848,4a878478a4c84,4a04a80a4804b8,a84840b8a48787,b87a48487a8b4,a7a80a474a84848,a87d804a8a48,a84078487a4a878
15 10 a84078487a4a878,a84c804a84b8,a8707a8474878a7,8a40a4a87807848,4a878478a4c84,4a04a80a4804b8,a84840b8a48787,b87a48487a8b4,a7a80a474a84848,a87d804a8a48
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=small
Transpose SIMONSARCRLEIMAGE
10 8 1c7597a9,19c75727,9a7a1752a7,d792571,a719b1651,a2b717269,c1b7272,b79b71a7
8 10 a19a7217,79b7217,c71717,a7179719,a7171b7,5a79a1a7,9a521b7,a7256a21,92a756a7,9a7a1927
dataset=image_deserialize group=transpose image_width=small image_height=small
Simon-ARC-RLE-Image remove duplicate adjacent rows
8 20 6a190106,96a09301,1a0603a0,a0b6301,6a06a030,9101a056,a606a050,a1060501,b010506,c05b0,01605a01,09050610,6a051b0,6050a101,605b010,051d0,05a01096,56101616,c01090,a109c0
8 20 6a190106,96a09301,1a0603a0,a0b6301,6a06a030,9101a056,a606a050,a1060501,b010506,c05b0,01605a01,09050610,6a051b0,6050a101,605b010,051d0,05a01096,56101616,c01090,a109c0
dataset=image_deserialize group=compress_y image_width=small image_height=medium
Histogram after deserializing Simons-ARC-RLE-Image
7 7 8a24a82,b848a2,8a54828,a845b8,a248282,a848b2,a828248
8:24,2:15,4:7,5:3
dataset=image_deserialize group=histogram image_width=small image_height=small
SIMONARCIMAGE, return translated y+1
13 9 9,b9a7g9,c97g9,d9a7e9,f97d9,9,,,
13 9 9,,b9a7g9,c97g9,d9a7e9,f97d9,9,,
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=small
simons-arc-image, 3x3 area, locations where all neighbors have the same color as center
19 14 b92949a24729524b9,b92c979a295a4942,7b942492427a254942,942a79a2a79272529a4,2a79249492c957947,b2a7c294a9a25b2,24a92a792a92b95a97,2972a92a7a972929574,79b797a9b74a29529,a27497a92979c75a7,a92a9a497249a2a7452,942b942a494b92a79,9724972924279272a92,2a7e942a92a9b4
19 14 0,,,,,,,,,,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=medium image_height=medium
CompressY SIMONARCIMAGE
6 3 b58a5,8,b8585
6 3 b58a5,8,b8585
dataset=image_deserialize group=compress_y image_width=small image_height=small
flipy simons-arc-image
2 5 24,60,89,9,94
2 5 94,9,89,60,24
dataset=image_deserialize group=flipy image_width=small image_height=small
SIMONARCIMAGE, identify pixels where exactly 6 neighbors have the same color as the center pixel
20 13 a6161b610a2c6a1a6,d610b261e616,d6a2d61f6,b6a2g6a161b6,6a16a161b6a1b6a1a6,61a61e61a61d6,c61j61a61,1h6161c61a6,c6a1h61b61,f6161a61f6,a61b61l6,b1h61f6,b61c61h616
20 13 0,b01k01b0,b01d0b1a0a101a0,01d01k0,e01b01h0,e01b01a01c010,010101c010a1010a10,b01f01b01c0,e010b10a1a01010,b0a1b01j0,b01b0a1a0b1e0,f01k0,0
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=medium
FlipY SIMONARCIMAGE
21 11 e3535f30b30,c50305f35d3,30c353b5b35353530,a5b3b5b35a0c30a3,c3535f35b3035,a30c5c30f353,b3535e35f30,a35a35a35e3a530a3,35b35a35c3050d3,5a35b353a5c3530353,a3535c3a0b35a30535
21 11 a3535c3a0b35a30535,5a35b353a5c3530353,35b35a35c3050d3,a35a35a35e3a530a3,b3535e35f30,a30c5c30f353,c3535f35b3035,a5b3b5b35a0c30a3,30c353b5b35353530,c50305f35d3,e3535f30b30
dataset=image_deserialize group=flipy image_width=large image_height=medium
SIMONSARCRLEIMAGE, 3x3 area, positions where all neighbors have the same color as center
4 15 a090,b09,b03,b09,0,,a030,04a0,07a0,,3730,0730,07a0,,0
4 15 0,,01a0,,,0,,,,,,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=small image_height=medium
Process SIMONSARCIMAGE and return the clockwise rotated
5 8 1,a15a1,,1c8,1,,,
8 5 1,c18b1,c18a51,c18b1,
dataset=image_deserialize group=rotate_cw image_width=small image_height=small
Convert Simons-ARC-RLE-Image and return histogram
6 6 a56a57,876050,687a56,06a865,a56085,c658
5:12,6:11,8:6,0:4,7:3
dataset=image_deserialize group=histogram image_width=small image_height=small
Simons-ARC-Image Compress-X
1 17 1,,,,,,0,,,,6,,4,,,,7
1 17 1,,,,,,0,,,,6,,4,,,,7
dataset=image_deserialize group=compress_x image_width=small image_height=medium
Get column 4 as digits, SIMONSARCRLEIMAGE
6 17 8b363,6a32a3,b3838,31c3,3160a3,3130a3,616083,3210a2,a31208,a31603,a31303,21a303,c603,2c32,32a323,3,
63333382000003233
dataset=image_deserialize group=get_column_as_list image_width=small image_height=medium
Convert SIMONSARCRLEIMAGE and return histogram
9 10 4343240a3,4632a42a3,746b3402,7432a4230,37a03b23,072a34392,327a34b8,a3720c3,d34230,4a02043a4
3:34,4:18,2:15,0:11,7:6,8:3,6:2,9:1
dataset=image_deserialize group=histogram image_width=small image_height=small
FlipY SIMONARCIMAGE
8 9 909a5a94,90295a94,98027924,a7092024,a96092a0,796972a7,565927a9,96a92a97,d97a9
8 9 d97a9,96a92a97,565927a9,796972a7,a96092a0,a7092024,98027924,90295a94,909a5a94
dataset=image_deserialize group=flipy image_width=small image_height=small
process SIMONSARCRLEIMAGE and return histogram
1 15 1,5,,,,,,,1,,,,,9,1
1:7,5:7,9:1
dataset=image_deserialize group=histogram image_width=small image_height=medium
Simons-ARC-Image, 3x3 area, locations where all neighbors have the same color as center
3 5 424,4a9,8a9,a89,849
3 5 0,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=small image_height=small
With Simon-ARC-RLE-Image, all pixels inside 3x3 have same color as center
4 7 6a86,2818,a818,6813,a613,6a16,6816
4 7 0,,,,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=small image_height=small
simons-arc-image, translate x by +1
8 15 86858698,6a857a86,685b698,a82a9b8,a82686a8,7286a868,82676a86,62a86b8,276d8,2a6869a6,68a6c8,6868a986,687a6869,a6a8a6a8,6d868
8 15 a8685869,a6a857a8,8685b69,b82a9a8,b826868,87286a86,682676a8,862a86a8,8276c8,62a68696,868a6b8,a6868a98,9687a686,8a6a8a68,86d86
dataset=image_deserialize group=translate_x_plus1 image_width=small image_height=medium
Get pixels from column 6, SIMONSARCRLEIMAGE
13 6 e97e9,g98c9,a9a5c986b9,c9c568679,a7a96a51a5169,a9b597a97b9
799557
dataset=image_deserialize group=get_column_as_list image_width=medium image_height=small
SIMONSARCRLEIMAGE, translate y by +1
12 20 f79c7,973d73737,b73479b797,c7c67873,a783a879b69,a93782a78797,c7826a7a98,8793821a69a7,b7481a79a78,797481a9c7,b78179a7387,9a7819e7,87a89d789,9a78979a78a7,97a9g7,a798a7978b7,7918g7,71973b79787,71d79b79,a73a7379c7
12 20 a73a7379c7,f79c7,973d73737,b73479b797,c7c67873,a783a879b69,a93782a78797,c7826a7a98,8793821a69a7,b7481a79a78,797481a9c7,b78179a7387,9a7819e7,87a89d789,9a78979a78a7,97a9g7,a798a7978b7,7918g7,71973b79787,71d79b79
dataset=image_deserialize group=translate_y_plus1 image_width=medium image_height=medium
Get column 5 as symbols, SIMONARCIMAGE
7 1 3c9a8
8
dataset=image_deserialize group=get_column_as_list image_width=small image_height=small
Process SIMONSARCRLEIMAGE and return the transposed
8 6 1,,b1a9b1,1,,
6 8 1,,,a19b1,,1,,
dataset=image_deserialize group=transpose image_width=small image_height=small
process Simon-ARC-RLE-Image and return flipy
16 3 c2b76g2,a202f7d2,2626a080d7b2
16 3 2626a080d7b2,a202f7d2,c2b76g2
dataset=image_deserialize group=flipy image_width=medium image_height=small
simons-arc-image Compress-Y
5 8 5a658,9a595,98a59,954a5,854a6,85a06,50896,95a96
5 8 5a658,9a595,98a59,954a5,854a6,85a06,50896,95a96
dataset=image_deserialize group=compress_y image_width=small image_height=small
Translate x plus 1, Simons-ARC-Image
4 7 4,b46,a464,48a4,4,,
4 7 4,6b4,b46,a484,4,,
dataset=image_deserialize group=translate_x_plus1 image_width=small image_height=small
histogram of deserialized SimonsRLEImage
7 10 4,,,,,,,,,
4:70
dataset=image_deserialize group=histogram image_width=small image_height=small
SimonsArcRleImage, 3x3 area, locations where all neighbors have the same color as center
11 4 8a61a689606,a68a629b61,0a61296a306,1c2a8a681
11 4 0,,,
dataset=image_deserialize group=all_neighbors_matching_center image_width=medium image_height=small
process SIMONARCIMAGE and return the cw rotated
18 19 3743a43434a3737374,a4a74a34b7b0a7a4,743b47c0b37272,a34c034347374a74,7a04727c47a42472,7a3434d74a3a437,7347a4a3473a4a3747,a7a353b47424a7a34,732757374373c424,7235a7b4342437437,7a35247a47474b342,4735347a47343d4,3754732a3a73a4b73,47572a72474a7372a3,47527472a72a4a37a4,25a4324a72a4a72a43,357c37343c4274,a37a32343a437a43a4,4732437a4274743b4
19 18 4a32a434f73743,73a5c73237a303a47,3a74b5b3234304374,2a34274b5737a40473,4b3727327a54370b4,3232473a4a73a420434,7a34a72a74343a707a3,a4a7a23b47437430a4,4a3a743e47a4073,2a42d7a3b743074,74342473a47437a4073,43b47347232a4a7303,a7474743d434a307,b47a3a4a347a347303,3a423a7437473424b7,432472743a437a47273,a47a437a432343b747,b434a3427a4a7242a4
dataset=image_deserialize group=rotate_cw image_width=medium image_height=medium
Simons-ARC-Image, translate x by +1
4 11 0297,a796,7590,0597,b15,5975,5619,2719,2a72,6a72,a726
4 11 7029,6a79,0759,7059,5b1,a597,9561,9271,a2a7,26a7,6a72
dataset=image_deserialize group=translate_x_plus1 image_width=small image_height=medium
convert SIMONSARCIMAGE and return the flipy
3 7 5a4,545,043,2a5,4a5,095,4a5
3 7 4a5,095,4a5,2a5,043,545,5a4
dataset=image_deserialize group=flipy image_width=small image_height=small
SIMONARCRLEIMAGE, identify pixels where exactly 1 neighbors have the same color as the center pixel
18 2 b0a9070a7a07b090,0709a09b09a09a097
18 2 g0a1e010,o010
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=medium image_height=small
CompressXY Simons-ARC-Image
10 9 a3168683a6,3a68686131,3836319183,3186a91361,3a893b1a6,163a61a631,813a616818,8386a16161,8c636183
10 9 a3168683a6,3a68686131,3836319183,3186a91361,3a893b1a6,163a61a631,813a616818,8386a16161,8c636183
dataset=image_deserialize group=compress_xy image_width=small image_height=small
move left by 1 pixel simons-arc-image
8 21 4,,,,b40c4,,a40c45,a40a4a54,40a45b4,4045c4,0a5d4,0f4,4,,,,,,,,
8 21 4,,,,a40d4,,40c454,40a4a5a4,0a45c4,045d4,a5d40,f40,4,,,,,,,,
dataset=image_deserialize group=translate_x_minus1 image_width=small image_height=large
CompressXY Simon-ARC-RLE-Image
4 8 10a2,a3a1,3101,a101,,1,,
4 5 10a2,a3a1,3101,a101,1
dataset=image_deserialize group=compress_xy image_width=small image_height=small
Simon-ARC-RLE-Image, move down by 1 pixel
8 10 e515,5656a515,61a5a1a5,a565b15,b5a1565,a1615a15,a51d5,561a51a5,5a1d5,c1a515
8 10 c1a515,e515,5656a515,61a5a1a5,a565b15,b5a1565,a1615a15,a51d5,561a51a5,5a1d5
dataset=image_deserialize group=translate_y_plus1 image_width=small image_height=small
SIMONARCIMAGE, 3x3 area, positions where all neighbors have the same color as center
17 15 7,,,,,,,,,,,,,,
17 15 0,0n10,,,,,,,,,,,,,0
dataset=image_deserialize group=all_neighbors_matching_center image_width=medium image_height=medium
Translate x-1 SimonsArcRleImage
7 3 a9c53,b3a9a3,34b394
7 3 9c539,a3a9b3,4b3943
dataset=image_deserialize group=translate_x_minus1 image_width=small image_height=small
Histogram of deserialized SIMONARCIMAGE
10 18 b04a04204,b0a4a04a0,b0202c0,02a04a02a0,a04b042a0,d042040,b02642b0,b01620404,2401602020,40150602a0,a01246a204,015a062a02,21b06c0,1a2a06a020,02c06b0,02b046a40,0240406b0,2d02a04
0:108,2:28,4:24,6:11,1:7,5:2
dataset=image_deserialize group=histogram image_width=small image_height=medium
Simon-ARC-RLE-Image rotated halfway
1 15 9,2,,,6,,,,5,,,,,8,0
1 15 0,8,5,,,,,6,,,,2,,,9
dataset=image_deserialize group=rotate_180 image_width=small image_height=medium
Simons-ARC-RLE-Image, identify pixels where exactly 4 neighbors have the same color as the center pixel
5 8 a9848,a6848,64843,59840,98084,98654,58564,6b94
5 8 0,,,,,,,
dataset=image_deserialize group=pixels_with_k_matching_neighbors image_width=small image_height=small
Rotate cw SimonsRLEImage
20 2 f4a9a5c258b2,a5c25f4d92
2 20 54,,24,,,,54,49,,45,,42,,,92,95,98,92,,2
dataset=image_deserialize group=rotate_cw image_width=medium image_height=small
With SIMONSARCRLEIMAGE, 3x3 area, where all pixels have same color as center
14 12 a7a070c19b8,7d17a0980a7,a0707a079a0b7,7c0707970a70,c07079a07b0,d079a0d7,07a079a0a707a0,a0a79b0a7c0,a0797b0707b0,0709b07a07a07,079f0a707,09a7e0a7a0
14 12 0,,,,a01j0,0,,,,,d01g0,0
dataset=image_deserialize group=all_neighbors_matching_center image_width=medium image_height=medium
Compress X and Y SIMONSARCRLEIMAGE
21 18 676a060d67a6a0c6,0a606a0a60e6070a6,0c67a670c6060c6,606707b6060b6a7a606,06706067c6b0b60a6,a67a670a6060b6a06706,7b0d6b0b607a6a0,a0a67a06b0a70a670676,067b60f67d60,a60b6a0a60c60b6a0,a060b6a060g606,7b67b60d60a67b6,0a60e6a0f6a0,06070606a0c67c676,a60g6760a60a6a0,0c6a0a60h676,70b60d606060a67a6,a0a60b60d67b6060
21 18 676a060d67a6a0c6,0a606a0a60e6070a6,0c67a670c6060c6,606707b6060b6a7a606,06706067c6b0b60a6,a67a670a6060b6a06706,7b0d6b0b607a6a0,a0a67a06b0a70a670676,067b60f67d60,a60b6a0a60c60b6a0,a060b6a060g606,7b67b60d60a67b6,0a60e6a0f6a0,06070606a0c67c676,a60g6760a60a6a0,0c6a0a60h676,70b60d606060a67a6,a0a60b60d67b6060
dataset=image_deserialize group=compress_xy image_width=large image_height=medium
SIMONSARCRLEIMAGE, translate x by -1
7 11 861a248,5861748,6597141,6859468,1a75496,7678489,67a6456,68641a5,a6545a6,b74a68,b68a68
7 11 61a24a8,8617485,5971416,8594686,a754961,6784897,7a645a6,8641a56,6545b6,a74a687,a68a686
dataset=image_deserialize group=translate_x_minus1 image_width=small image_height=medium
Get column 0 as digits, Simons-ARC-RLE-Image
2 16 5,53,5,35,,3,35,3,53,3,5,35,3,5,53,35
5553333353533553
dataset=image_deserialize group=get_column_as_list image_width=small image_height=medium
process Simons-ARC-RLE-Image and return the ccw rotated
20 18 7b4a7c47b47a47a4,70e47b474704747,0b78f47a4b747,40a47847a4a73b470a4,0474a787c430d47,7d484a743a4a747a4,747a4748a473a7b4747,70d7484a3a47b4a7,4e7487a37a40a7a4,747c4a7843c47b4,4747d4717074747a4,c407a4a1b40a7a474,b47a4a17d409c4,d4f647a4a947,7b4704707a4e697,747a470a4b74747b47,c7474a7c47a47b4,a4a707b4a7407a4b74
18 20 4a7474a7d4b7a4,f47b47a49a47,b704a74747a496a47,a4a7c4a7b4964a7,407a47a404a79467a4,a7b4747b47046b4,c4047b470476b7,4a7a3474740b46a40,b474d37a4647a4,7a47a47a341a464747,d47a4787146a747,47b474a8741760474,b4a748a47a4167474,c4a84a7b41640a4,7a4874b7a47460b7,748a7a4a7a40a47a40,a47c4a74747b4a7,a47474c7d4b7,4070b40747d474,a7040b747c4b74
dataset=image_deserialize group=rotate_ccw image_width=medium image_height=medium
SimonsRLEImage rotated by 180 degrees
21 3 45b7a05a4a95b4549a4,a95a04a9494545e45,4905a94a945b4a9495a4
21 3 a4594a9b454a94a95094,5e4545494a94a05a9,a4945b45a9a45a0b754
dataset=image_deserialize group=rotate_180 image_width=large image_height=small
Simons-ARC-RLE-Image Compress-Y
11 15 b56e56,56c54c5,59c541b5,a5a9b54565,c59a541a5,a56a5a954a5,c56a5a915,b5a6c595,h561,d56c51,56h5,6i5,c5656c5,f56b5,5
11 15 b56e56,56c54c5,59c541b5,a5a9b54565,c59a541a5,a56a5a954a5,c56a5a915,b5a6c595,h561,d56c51,56h5,6i5,c5656c5,f56b5,5
dataset=image_deserialize group=compress_y image_width=medium image_height=medium
Compress X and Y SIMONARCIMAGE
8 3 05a08a50,d9050,050521a0
8 3 05a08a50,d9050,050521a0
dataset=image_deserialize group=compress_xy image_width=small image_height=small
convert Simon-ARC-RLE-Image and return the transposed
3 15 4,a49,,,494,498,,,468,968,,,,648,6a1
15 3 h4c9a6,c4c9d641,4b94h81
dataset=image_deserialize group=transpose image_width=small image_height=medium
move right by 1 pixel SimonsArcRleImage
9 3 c3b9a3,3,f323
9 3 d3b93,3,g32
dataset=image_deserialize group=translate_x_plus1 image_width=small image_height=small
flipx SIMONSARCRLEIMAGE
5 12 05a05,b010,3a010,23195,20390,21a30,2a409,2b09,c09,,,0
5 12 5a050,01b0,01a03,59132,09302,0a312,90a42,9b02,9c0,,,0
dataset=image_deserialize group=flipx image_width=small image_height=medium
process Simon-ARC-RLE-Image and return the cw rotated
14 6 91259515a15141,156a2919a59b8,a16a1a2c8b1,165c8a29b15,968d19a1515,15a19a19a195a1
6 14 19b19,5a6151,185a62,a18125,918129,a18295,a182a1,912895,192851,a19851,9a1895,a5a181,c184,1a5181
dataset=image_deserialize group=rotate_cw image_width=medium image_height=small
CompressY SimonsArcRleImage
2 1 8
2 1 8
dataset=image_deserialize group=compress_y image_width=small image_height=small
rotate 180 Simons-ARC-RLE-Image
15 11 e36g3,f3a6e3,h3a6c3,j36b3,3,,,,,,
15 11 3,,,,,,,b36j3,c3a6h3,e3a6f3,g36e3
dataset=image_deserialize group=rotate_180 image_width=medium image_height=medium
process Simon-ARC-RLE-Image and return the transposed
19 20 505050c5050503505,a5a0a505c05043a05,a0b5b0c50453050,d050d540535a0,0b505c0540a53505,e50b54a0503b5,b05b0a545a0503505,b5c054a50b53a50,c5050405a0a503a50,a505a040b53a5030a5,a0a504b0535a0530a5,b054a05a3a050a1a50,a0545a0305a0a1a0a50,504b03505a1b50b5,540503a0a150a505050,4b535a1b5b0505a0,50a3a1b05a05a0a505,03a1050a50a5a05c0,31b0a505e0c5,505b0a5b0a5a05b0
20 19 a5b050b5b0a545035,05a0a50b5c0450310,5050a50a505054053105,a050b50c540a531a0,b5a05d045a031b0,050b5a0504b0351a50,5g04b0301a0a5,a5050b54a0535010505,50a50a540503a0150a50,50a5054c53a51a5b0,a0b54a5053a01a505a0,50a54c035a01b0505,0504b0b5051a505a05,5040e5a01a5d0,04b5a05a051050505a0,j31a05050a5,5a0e5a0b50a5050,a05a050g5b050,a5a0b5a0a5a05a05050
dataset=image_deserialize group=transpose image_width=medium image_height=medium
Simons-ARC-Image rotated halfway
16 17 a59a535b36535a3,a395a354b3b5a3,a69b3945b35b3,a39b39345c363,595a393545b3536,515a39b54d35,59189a35645a65a3,a63a1b3634b365,3659318a354a3535,56935318a3543b7,369c31a8b7a13,69a3a5a315851a35,69b363a5b18b3,935b36a3b13856,a36a353515a316a3,a36a35a15d435,635351b4536b35
16 17 5b3635b4153536,53d45a15a36a3,a361a351535a36a3,6583b1a36b3539,b38b1a536b396,5a315851a3a5a396,3a1b7a81c3963,b7345a381353965,535a345a38139563,56b3436b3a13a6,a35a65465a398195,5d34b59a3515,635b354539a3595,36c35439b39a3,b35b3549b39a6,a3b5b345a359a3,a35356b353a59a5
dataset=image_deserialize group=rotate_180 image_width=medium image_height=medium
End of preview. Expand in Data Studio

Version 1

Have dataset items that are somewhat evenly of each type. The LLM learned some of the types fine. However rotated images are causing problems. The image sizes are between 1 and 10 pixels.

Version 2

Here the majority of dataset items are rotated images. Since this is what my LLM is struggling with. Smaller images. Here the image sizes are between 1 and 5 pixels. This helped a lot on the validation loss.

Version 3

Main focus is now on count_same_color_as_center_with_8neighbors_nowrap and image size 1-6. Which the LLM has struggeld with in the past, maybe due to too big image sizes. Struggles somewhat with the count_same_color_as_center_with_8neighbors_nowrap.

Version 4

I'm trying smaller images again. Here the image sizes are between 1 and 5 pixels. Added same_color_inside_3x3_area_nowrap that checks if all surrounding pixels agree on the same color, maybe that have some synergy with the count_same_color_as_center_with_8neighbors_nowrap. It helped a little, but it's still not as good at counting neighbors as I would like.

Version 5

I have added a pixels_with_k_matching_neighbors with a k parameter between 1-8. This may help improve on counting the number of neighboring pixels. The image size 1-6. This did indeed help on counting the number of surrounding pixels.

Version 6

Same weight to all the transformations. Image size 1-11.

Version 7

Focus on histogram and k-nearest neighbors. image size 1-12. It seems like the LLM has gotten the hang of it.

Version 8

Focus on histogram and k-nearest neighbors. image size 5-20.

Version 9

Focus on histogram and k-nearest neighbors. image size 10-30.

Version 10

Same weight to all the transformations. image width 10-30. image height 2-5.

Version 11

Same weight to all the transformations. image width 2-5. image height 10-30.

Version 12

Focus on k-nearest neighbors. image width 2-5. image height 10-30.

Version 13

Focus on compres_x, compres_y, compres_xy. image size is 1-10.

Version 14

Focus on histograms and k-nearest-neighbors. image size 5-20.

Version 15

Focus on histograms and k-nearest-neighbors. image size 10-30.

Version 16

Focus on k-nearest-neighbors. image size 10-25.

Version 17

Disabled k-nearest-neighbors, I suspect this is the reason why it converges so slowly. image size 15-30.

Version 18

Disabled k-nearest-neighbors, and compression. image size 15-25.

Version 19

Translate x/y by plus/minus 1. Disabled rotation and transpose. image size 22-30.

Version 20

Focus on k-nearest-neighbors. image size 5-15.

Version 21

Focus on k-nearest-neighbors. image size 8-18.

Version 22

Same weight to all the transformations. image size 8-20.

Version 23

Same weight to all the transformations. image size 5-30.

The LLM is struggling learning this. I'm going to try with small images.

Version 24

Focus on rotate cw, rotate ccw, transpose. image size 2-10.

The LLM is struggling learning this. Despite being small images. I'm going to try with even small images.

Version 25

Focus on rotate cw, rotate ccw, transpose. image size 2-5.

The LLM is struggling learning this. Despite being small images. I'm going to try with even small images.

Version 26

Focus on rotate cw, rotate ccw, transpose, k-nearest-neighbors. image size 1-3.

The LLM is struggling learning this. Despite being small images.

Version 27

Focus on rotate cw, rotate ccw, transpose. image size 1-4.

The LLM is struggling learning this. Despite being small images.

Version 28

Focus on rotate cw, rotate ccw, transpose. image size 1-5.

Version 29

Focus on rotate cw, rotate ccw, transpose. image size 1-6.

Version 30

Focus on rotate cw, rotate ccw, transpose. image size 1-8.

Version 31

Focus on rotate cw, rotate ccw, transpose. image size 1-10.

Version 32

Focus on rotate cw, rotate ccw, transpose. image size 1-12.

Version 33

Focus on rotate cw, rotate ccw, transpose. image size 1-14. The serialize items were using fewer names to identify the dataset, now uses the same names as deserialize.

Version 34

Focus only on rotate cw. All other operations have been disabled. image size 1-30.

Version 35

Focus only on rotate ccw. All other operations have been disabled. image size 1-30.

Version 36

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-30.

Version 37

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-30. Using the same image and apply both rotate cw and rotate ccw. My hypothesis is that it will learn better to distinguish between the two rotation types.

Version 38

Argh, the validation loss was seriously bad on this one.

I guess what happened is that rotate cw always was followed by rotate ccw, causing the model to be biased, always expecting the opposite transformation. Now I have suffled the entire dataset. So there are still 50% of each operation, in random order.

Using the same image and apply both rotate cw and rotate ccw. I still think my hypothesis is sound, that it will learn better to distinguish between the two rotation types when it's the same image.

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-30.

Version 39

The validation loss is not improving.

I'm going back to small image sizes.

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-10.

This helped on the validation loss. Yay.

Version 40

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-13.

Version 41

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-16.

Version 42

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-19.

Jumping to image size 19 was a bit too optimistic, causing a terrible validation loss. So I have to go with a lower image size.

Version 43

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-17.

Version 44

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-18.

Version 45

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-19.

Version 46

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-20. This is something that the LLM struggles with. I'm going to make a dataset with another random seed, with the same size 1-20, to see if it improves or worsens.

Version 47

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-20. Same size as in previous version.

Version 48

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-21. This is something that the LLM struggles with. I'm going to make a dataset with another random seed, with the same size 1-21, to see if it improves or worsens.

Version 49

Focus only on rotate cw and rotate ccw. All other operations have been disabled. image size 1-21. Same size as in previous version. This is something that the LLM struggles with. Not improving.

Version 50

Focus only on get_row_as_list and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-21. This was first time the LLM tried this, so terrible validation loss. I'm going to try smaller images.

Version 51

Focus only on get_row_as_list and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-5. Excellent validation loss.

Version 52

Focus only on get_row_as_list and get_column_as_list and rotate cw and rotate ccw. All other operations have been disabled. image size 1-10. No problems for the LLM to learn that. I'm not going to train it to the end.

Version 53

Focus only on get_row_as_list and get_column_as_list and rotate cw and rotate ccw. All other operations have been disabled. image size 1-15.

Version 54

Focus only on get_row_as_list and get_column_as_list and rotate cw and rotate ccw. All other operations have been disabled. image size 1-20.

Version 55

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-22.

Version 56

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-23. This is something that the LLM struggles with. Not improving.

Version 57

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-24. This is something that the LLM struggles with. Not improving.

Version 58

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-30. This is something that the LLM struggles with. Not improving.

Version 59

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-25.

Version 60

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-20. The model is good at this. I'm going to try with a bigger size.

Version 61

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-22.

Version 62

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-22. Training on same size again.

Version 63

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-22. Training on same size again. Slowly improving the validation loss.

Version 64

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-21.

Version 65

Focus only on rotate cw and rotate ccw and get_column_as_list. All other operations have been disabled. The get_column_as_list is related to rotating the image. image size 1-21. Using different images.

Version 66

Same weight to all the transformations. image size 1-21.

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