{"action":"created","issue":{"url":"https://api.github.com/repos/pjreddie/darknet/issues/611","repository_url":"https://api.github.com/repos/pjreddie/darknet","labels_url":"https://api.github.com/repos/pjreddie/darknet/issues/611/labels{/name}","comments_url":"https://api.github.com/repos/pjreddie/darknet/issues/611/comments","events_url":"https://api.github.com/repos/pjreddie/darknet/issues/611/events","html_url":"https://github.com/pjreddie/darknet/issues/611","id":310273056,"node_id":"MDU6SXNzdWUzMTAyNzMwNTY=","number":611,"title":"cannot load image","user":{"login":"pjryan513","id":5874809,"node_id":"MDQ6VXNlcjU4NzQ4MDk=","avatar_url":"https://avatars0.githubusercontent.com/u/5874809?v=4","gravatar_id":"","url":"https://api.github.com/users/pjryan513","html_url":"https://github.com/pjryan513","followers_url":"https://api.github.com/users/pjryan513/followers","following_url":"https://api.github.com/users/pjryan513/following{/other_user}","gists_url":"https://api.github.com/users/pjryan513/gists{/gist_id}","starred_url":"https://api.github.com/users/pjryan513/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pjryan513/subscriptions","organizations_url":"https://api.github.com/users/pjryan513/orgs","repos_url":"https://api.github.com/users/pjryan513/repos","events_url":"https://api.github.com/users/pjryan513/events{/privacy}","received_events_url":"https://api.github.com/users/pjryan513/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":23,"created_at":"2018-03-31T21:20:54Z","updated_at":"2018-07-16T13:42:34Z","closed_at":null,"author_association":"NONE","body":"I am training on my own dataset and I keep getting the error: **\"annot load image\"** for the .jpg files I want to train on and **Couldn't open file** for the .txt files for the bounding box info.  I was following the advice of a similar question [here](https://github.com/pjreddie/darknet/issues/478).  I added the images and labels folders putting the .jpg in images and the .txt in labels.  I still get the same errors though and have tried both yolo2 and yolo3.\r\n\r\nmy obj.data file is called **phallusia.data**, location is **darknet/data/phallusia.data** and the contents are\r\n```\r\nclasses= 1\r\ntrain = data/train.txt\r\nvalid = data/test.txt\r\nnames = data/phallusia.names\r\nbackup = backup/\r\n```\r\n \r\nmy train.txt location is **darknet/data/train.txt**, the contents are\r\n```\r\ndata/phallusia/images/v1scene00001.jpg\r\ndata/phallusia/images/v1scene00051.jpg\r\ndata/phallusia/images/v1scene00101.jpg\r\n\r\n```\r\nmy test.txt location is **darknet/data/test.txt**, the contents are\r\n```\r\ndata/phallusia/images/v4scene03051.jpg\r\ndata/phallusia/images/v4scene03101.jpg\r\ndata/phallusia/images/v4scene03151.jpg\r\n\r\n```\r\nmy cfg/yolo-phallusia.cfg contains, \r\n```\r\n[net]\r\nbatch=64\r\nsubdivisions=8\r\nheight=416\r\nwidth=416\r\nchannels=3\r\nmomentum=0.9\r\ndecay=0.0005\r\nangle=0\r\nsaturation = 1.5\r\nexposure = 1.5\r\nhue=.1\r\n\r\nlearning_rate=0.0001\r\nmax_batches = 45000\r\npolicy=steps\r\nsteps=100,25000,35000\r\nscales=10,.1,.1\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=32\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[maxpool]\r\nsize=2\r\nstride=2\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=64\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[maxpool]\r\nsize=2\r\nstride=2\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=128\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=64\r\nsize=1\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=128\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[maxpool]\r\nsize=2\r\nstride=2\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=256\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=128\r\nsize=1\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=256\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[maxpool]\r\nsize=2\r\nstride=2\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=512\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=256\r\nsize=1\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=512\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=256\r\nsize=1\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=512\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[maxpool]\r\nsize=2\r\nstride=2\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=1024\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=512\r\nsize=1\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=1024\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=512\r\nsize=1\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nfilters=1024\r\nsize=3\r\nstride=1\r\npad=1\r\nactivation=leaky\r\n\r\n\r\n#######\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nsize=3\r\nstride=1\r\npad=1\r\nfilters=1024\r\nactivation=leaky\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nsize=3\r\nstride=1\r\npad=1\r\nfilters=1024\r\nactivation=leaky\r\n\r\n[route]\r\nlayers=-9\r\n\r\n[reorg]\r\nstride=2\r\n\r\n[route]\r\nlayers=-1,-3\r\n\r\n[convolutional]\r\nbatch_normalize=1\r\nsize=3\r\nstride=1\r\npad=1\r\nfilters=1024\r\nactivation=leaky\r\n\r\n[convolutional]\r\nsize=1\r\nstride=1\r\npad=1\r\nfilters=30\r\nactivation=linear\r\n\r\n[region]\r\nanchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52\r\nbias_match=1\r\nclasses=1\r\ncoords=4\r\nnum=5\r\nsoftmax=1\r\njitter=.2\r\nrescore=1\r\n\r\nobject_scale=5\r\nnoobject_scale=1\r\nclass_scale=1\r\ncoord_scale=1\r\n\r\nabsolute=1\r\nthresh = .6\r\nrandom=0\r\n```\r\nthe path for darkent is \r\n**/home/chemistry/Patricks Work/v3/darknet**, should I move it to home directory?\r\nthe path for data folder is \r\n**/home/chemistry/Patricks Work/v3/darknet/data**\r\nPath to images is \r\n**/home/chemistry/Patricks Work/v3/darknet/data/phallusia/images** \r\npath to labels is \r\n**/home/chemistry/Patricks Work/v3/darknet/data/phallusia/labels** \r\ndid I put the folders in the right areas?  \r\n\r\nHopefully this is enough info to figure out what I am doing wrong.\r\n"},"comment":{"url":"https://api.github.com/repos/pjreddie/darknet/issues/comments/405251437","html_url":"https://github.com/pjreddie/darknet/issues/611#issuecomment-405251437","issue_url":"https://api.github.com/repos/pjreddie/darknet/issues/611","id":405251437,"node_id":"MDEyOklzc3VlQ29tbWVudDQwNTI1MTQzNw==","user":{"login":"jmcmahon994","id":2073310,"node_id":"MDQ6VXNlcjIwNzMzMTA=","avatar_url":"https://avatars0.githubusercontent.com/u/2073310?v=4","gravatar_id":"","url":"https://api.github.com/users/jmcmahon994","html_url":"https://github.com/jmcmahon994","followers_url":"https://api.github.com/users/jmcmahon994/followers","following_url":"https://api.github.com/users/jmcmahon994/following{/other_user}","gists_url":"https://api.github.com/users/jmcmahon994/gists{/gist_id}","starred_url":"https://api.github.com/users/jmcmahon994/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jmcmahon994/subscriptions","organizations_url":"https://api.github.com/users/jmcmahon994/orgs","repos_url":"https://api.github.com/users/jmcmahon994/repos","events_url":"https://api.github.com/users/jmcmahon994/events{/privacy}","received_events_url":"https://api.github.com/users/jmcmahon994/received_events","type":"User","site_admin":false},"created_at":"2018-07-16T13:42:34Z","updated_at":"2018-07-16T13:42:34Z","author_association":"NONE","body":"@zboinek \r\n@kylynf \r\n\r\nHere is an extract of my training output. It took about 5 minutes to perform 154 iterations.\r\n\r\nI compiled YOLO with GPU=1 set in the makefile. To do this I first had to install the CUDA Toolkit. My GPU is the NVIDIA GeForce GTX 1060 (which is CUDA compatible).\r\n\r\nA .backup file and .weights file were saved to the backup folder after 100 iterations. These are both the same weights. After another 100 iterations the .backup file will be updated and another .weights file is created.\r\n\r\n`jason@Jason-Ubuntu:~/darknet$ ./darknet detector train NFPA/nfpa.data NFPA/yolov2-tiny-voc.cfg darknet19_448.conv.23 \r\nyolov2-tiny-voc\r\nlayer     filters    size              input                output\r\n    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs\r\n    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16\r\n    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32  0.399 BFLOPs\r\n    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32\r\n    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64  0.399 BFLOPs\r\n    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64\r\n    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128  0.399 BFLOPs\r\n    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128\r\n    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256  0.399 BFLOPs\r\n    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256\r\n   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs\r\n   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512\r\n   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024  1.595 BFLOPs\r\n   13 conv   1024  3 x 3 / 1    13 x  13 x1024   ->    13 x  13 x1024  3.190 BFLOPs\r\n   14 conv     30  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x  30  0.010 BFLOPs\r\n   15 detection\r\nmask_scale: Using default '1.000000'\r\nLoading weights from darknet19_448.conv.23...Done!\r\nLearning Rate: 0.001, Momentum: 0.9, Decay: 0.0005\r\nResizing\r\n384\r\nLoaded: 0.000034 seconds\r\nRegion Avg IOU: 0.591076, Class: 1.000000, Obj: 0.499727, No Obj: 0.500027, Avg Recall: 0.800000,  count: 10\r\nRegion Avg IOU: 0.342480, Class: 1.000000, Obj: 0.499824, No Obj: 0.500030, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.398422, Class: 1.000000, Obj: 0.499315, No Obj: 0.500029, Avg Recall: 0.428571,  count: 14\r\nRegion Avg IOU: 0.301751, Class: 1.000000, Obj: 0.499156, No Obj: 0.500028, Avg Recall: 0.400000,  count: 10\r\nRegion Avg IOU: 0.422285, Class: 1.000000, Obj: 0.499392, No Obj: 0.500029, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.234298, Class: 1.000000, Obj: 0.499153, No Obj: 0.500028, Avg Recall: 0.178571,  count: 28\r\nRegion Avg IOU: 0.379142, Class: 1.000000, Obj: 0.499205, No Obj: 0.500026, Avg Recall: 0.400000,  count: 10\r\nRegion Avg IOU: 0.221257, Class: 1.000000, Obj: 0.499537, No Obj: 0.500027, Avg Recall: 0.250000,  count: 8\r\n1: 196.524750, 196.524750 avg, 0.001000 rate, 1.498131 seconds, 64 images\r\nLoaded: 0.000041 seconds\r\nRegion Avg IOU: 0.443352, Class: 1.000000, Obj: 0.495614, No Obj: 0.495432, Avg Recall: 0.625000,  count: 8\r\nRegion Avg IOU: 0.540963, Class: 1.000000, Obj: 0.495293, No Obj: 0.495435, Avg Recall: 0.875000,  count: 8\r\nRegion Avg IOU: 0.356452, Class: 1.000000, Obj: 0.495300, No Obj: 0.495434, Avg Recall: 0.555556,  count: 9\r\nRegion Avg IOU: 0.544373, Class: 1.000000, Obj: 0.495129, No Obj: 0.495431, Avg Recall: 0.818182,  count: 11\r\nRegion Avg IOU: 0.315706, Class: 1.000000, Obj: 0.494998, No Obj: 0.495432, Avg Recall: 0.444444,  count: 9\r\nRegion Avg IOU: 0.358486, Class: 1.000000, Obj: 0.494840, No Obj: 0.495434, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.453712, Class: 1.000000, Obj: 0.495120, No Obj: 0.495432, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.468369, Class: 1.000000, Obj: 0.494803, No Obj: 0.495434, Avg Recall: 0.500000,  count: 14\r\n2: 185.430191, 195.415298 avg, 0.001000 rate, 1.092638 seconds, 128 images\r\nLoaded: 0.000033 seconds\r\nRegion Avg IOU: 0.369313, Class: 1.000000, Obj: 0.485858, No Obj: 0.486759, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.366365, Class: 1.000000, Obj: 0.486704, No Obj: 0.486759, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.400188, Class: 1.000000, Obj: 0.486290, No Obj: 0.486759, Avg Recall: 0.444444,  count: 9\r\nRegion Avg IOU: 0.241825, Class: 1.000000, Obj: 0.485891, No Obj: 0.486763, Avg Recall: 0.222222,  count: 9\r\nRegion Avg IOU: 0.431668, Class: 1.000000, Obj: 0.486198, No Obj: 0.486757, Avg Recall: 0.500000,  count: 12\r\nRegion Avg IOU: 0.544339, Class: 1.000000, Obj: 0.486478, No Obj: 0.486759, Avg Recall: 0.857143,  count: 7\r\nRegion Avg IOU: 0.487178, Class: 1.000000, Obj: 0.486183, No Obj: 0.486758, Avg Recall: 0.600000,  count: 10\r\nRegion Avg IOU: 0.427375, Class: 1.000000, Obj: 0.486401, No Obj: 0.486760, Avg Recall: 0.625000,  count: 8\r\n3: 181.007828, 193.974548 avg, 0.001000 rate, 1.135743 seconds, 192 images\r\n\r\n...\r\n\r\nLoaded: 0.000040 seconds\r\nRegion Avg IOU: 0.521117, Class: 1.000000, Obj: 0.016137, No Obj: 0.016265, Avg Recall: 0.750000,  count: 8\r\nRegion Avg IOU: 0.445559, Class: 1.000000, Obj: 0.016181, No Obj: 0.016267, Avg Recall: 0.555556,  count: 9\r\nRegion Avg IOU: 0.405584, Class: 1.000000, Obj: 0.016051, No Obj: 0.016259, Avg Recall: 0.555556,  count: 9\r\nRegion Avg IOU: 0.638974, Class: 1.000000, Obj: 0.016379, No Obj: 0.016254, Avg Recall: 0.875000,  count: 8\r\nRegion Avg IOU: 0.280449, Class: 1.000000, Obj: 0.016420, No Obj: 0.016266, Avg Recall: 0.111111,  count: 18\r\nRegion Avg IOU: 0.400591, Class: 1.000000, Obj: 0.016070, No Obj: 0.016266, Avg Recall: 0.555556,  count: 9\r\nRegion Avg IOU: 0.547657, Class: 1.000000, Obj: 0.016353, No Obj: 0.016258, Avg Recall: 0.750000,  count: 8\r\nRegion Avg IOU: 0.371884, Class: 1.000000, Obj: 0.016042, No Obj: 0.016262, Avg Recall: 0.500000,  count: 8\r\n100: 10.851920, 13.160944 avg, 0.001000 rate, 1.618434 seconds, 6400 images\r\nSaving weights to NFPA/backup/yolov2-tiny-voc.backup\r\nSaving weights to NFPA/backup/yolov2-tiny-voc_100.weights\r\n\r\n...\r\n\r\nLoaded: 0.000043 seconds\r\nRegion Avg IOU: 0.403313, Class: 1.000000, Obj: 0.014164, No Obj: 0.014287, Avg Recall: 0.400000,  count: 10\r\nRegion Avg IOU: 0.360459, Class: 1.000000, Obj: 0.014621, No Obj: 0.014292, Avg Recall: 0.333333,  count: 12\r\nRegion Avg IOU: 0.367700, Class: 1.000000, Obj: 0.013823, No Obj: 0.014286, Avg Recall: 0.500000,  count: 8\r\nRegion Avg IOU: 0.405575, Class: 1.000000, Obj: 0.013849, No Obj: 0.014278, Avg Recall: 0.375000,  count: 8\r\nRegion Avg IOU: 0.436118, Class: 1.000000, Obj: 0.013024, No Obj: 0.014293, Avg Recall: 0.555556,  count: 9\r\nRegion Avg IOU: 0.485750, Class: 1.000000, Obj: 0.014712, No Obj: 0.014294, Avg Recall: 0.642857,  count: 14\r\nRegion Avg IOU: 0.316758, Class: 1.000000, Obj: 0.013006, No Obj: 0.014288, Avg Recall: 0.375000,  count: 8\r\nRegion Avg IOU: 0.420868, Class: 1.000000, Obj: 0.012581, No Obj: 0.014285, Avg Recall: 0.500000,  count: 10\r\n153: 10.040620, 11.442871 avg, 0.001000 rate, 1.591577 seconds, 9792 images\r\nLoaded: 0.000037 seconds\r\nRegion Avg IOU: 0.386361, Class: 1.000000, Obj: 0.013367, No Obj: 0.014264, Avg Recall: 0.444444,  count: 9\r\nRegion Avg IOU: 0.461056, Class: 1.000000, Obj: 0.012953, No Obj: 0.014272, Avg Recall: 0.500000,  count: 10\r\nRegion Avg IOU: 0.294480, Class: 1.000000, Obj: 0.014540, No Obj: 0.014262, Avg Recall: 0.181818,  count: 33\r\nRegion Avg IOU: 0.366687, Class: 1.000000, Obj: 0.013329, No Obj: 0.014265, Avg Recall: 0.555556,  count: 9\r\nRegion Avg IOU: 0.216087, Class: 1.000000, Obj: 0.013347, No Obj: 0.014260, Avg Recall: 0.153846,  count: 13\r\nRegion Avg IOU: 0.279263, Class: 1.000000, Obj: 0.013682, No Obj: 0.014256, Avg Recall: 0.250000,  count: 8\r\nRegion Avg IOU: 0.477770, Class: 1.000000, Obj: 0.014243, No Obj: 0.014266, Avg Recall: 0.625000,  count: 8\r\nRegion Avg IOU: 0.378830, Class: 1.000000, Obj: 0.013621, No Obj: 0.014266, Avg Recall: 0.444444,  count: 9\r\n154: 11.001608, 11.398745 avg, 0.001000 rate, 1.569078 seconds, 9856 images'"}}
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