{"action":"created","issue":{"url":"https://api.github.com/repos/pjreddie/darknet/issues/377","repository_url":"https://api.github.com/repos/pjreddie/darknet","labels_url":"https://api.github.com/repos/pjreddie/darknet/issues/377/labels{/name}","comments_url":"https://api.github.com/repos/pjreddie/darknet/issues/377/comments","events_url":"https://api.github.com/repos/pjreddie/darknet/issues/377/events","html_url":"https://github.com/pjreddie/darknet/issues/377","id":283270052,"number":377,"title":"The Region Avg IOU is very small (Region Avg IOU: 0.009045) and the Avg Recall: 0.000000","user":{"login":"SteveIb","id":24913534,"avatar_url":"https://avatars2.githubusercontent.com/u/24913534?v=4","gravatar_id":"","url":"https://api.github.com/users/SteveIb","html_url":"https://github.com/SteveIb","followers_url":"https://api.github.com/users/SteveIb/followers","following_url":"https://api.github.com/users/SteveIb/following{/other_user}","gists_url":"https://api.github.com/users/SteveIb/gists{/gist_id}","starred_url":"https://api.github.com/users/SteveIb/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/SteveIb/subscriptions","organizations_url":"https://api.github.com/users/SteveIb/orgs","repos_url":"https://api.github.com/users/SteveIb/repos","events_url":"https://api.github.com/users/SteveIb/events{/privacy}","received_events_url":"https://api.github.com/users/SteveIb/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2017-12-19T15:27:46Z","updated_at":"2017-12-24T09:17:26Z","closed_at":null,"author_association":"NONE","body":"I have images 3000 * 3000 , and each image contains small objects 33*33, each image has roughly around 1000 objects, I followed this tutroial\r\nhttps://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/\r\n\r\nbut I still dont get results at all.. I tried different parameter, but still the same.\r\n\r\n./darknet detector train cfg/obj.data cfg/yolo-obj.cfg darknet19_448.conv.23\r\nyolo-obj\r\nlayer     filters    size              input                output\r\n    0 conv     32  3 x 3 / 1   806 x 806 x   3   ->   806 x 806 x  32\r\n    1 max          2 x 2 / 2   806 x 806 x  32   ->   403 x 403 x  32\r\n    2 conv     64  3 x 3 / 1   403 x 403 x  32   ->   403 x 403 x  64\r\n    3 max          2 x 2 / 2   403 x 403 x  64   ->   201 x 201 x  64\r\n    4 conv    128  3 x 3 / 1   201 x 201 x  64   ->   201 x 201 x 128\r\n    5 conv     64  1 x 1 / 1   201 x 201 x 128   ->   201 x 201 x  64\r\n    6 conv    128  3 x 3 / 1   201 x 201 x  64   ->   201 x 201 x 128\r\n    7 max          2 x 2 / 2   201 x 201 x 128   ->   100 x 100 x 128\r\n    8 conv    256  3 x 3 / 1   100 x 100 x 128   ->   100 x 100 x 256\r\n    9 conv    128  1 x 1 / 1   100 x 100 x 256   ->   100 x 100 x 128\r\n   10 conv    256  3 x 3 / 1   100 x 100 x 128   ->   100 x 100 x 256\r\n   11 max          2 x 2 / 2   100 x 100 x 256   ->    50 x  50 x 256\r\n   12 conv    512  3 x 3 / 1    50 x  50 x 256   ->    50 x  50 x 512\r\n   13 conv    256  1 x 1 / 1    50 x  50 x 512   ->    50 x  50 x 256\r\n   14 conv    512  3 x 3 / 1    50 x  50 x 256   ->    50 x  50 x 512\r\n   15 conv    256  1 x 1 / 1    50 x  50 x 512   ->    50 x  50 x 256\r\n   16 conv    512  3 x 3 / 1    50 x  50 x 256   ->    50 x  50 x 512\r\n   17 max          2 x 2 / 2    50 x  50 x 512   ->    25 x  25 x 512\r\n   18 conv   1024  3 x 3 / 1    25 x  25 x 512   ->    25 x  25 x1024\r\n   19 conv    512  1 x 1 / 1    25 x  25 x1024   ->    25 x  25 x 512\r\n   20 conv   1024  3 x 3 / 1    25 x  25 x 512   ->    25 x  25 x1024\r\n   21 conv    512  1 x 1 / 1    25 x  25 x1024   ->    25 x  25 x 512\r\n   22 conv   1024  3 x 3 / 1    25 x  25 x 512   ->    25 x  25 x1024\r\n   23 conv   1024  3 x 3 / 1    25 x  25 x1024   ->    25 x  25 x1024\r\n   24 conv   1024  3 x 3 / 1    25 x  25 x1024   ->    25 x  25 x1024\r\n   25 route  16\r\n   26 conv     64  1 x 1 / 1    50 x  50 x 512   ->    50 x  50 x  64\r\n   27 reorg              / 2    50 x  50 x  64   ->    25 x  25 x 256\r\n   28 route  27 24\r\n   29 conv   1024  3 x 3 / 1    25 x  25 x1280   ->    25 x  25 x1024\r\n   30 conv     30  1 x 1 / 1    25 x  25 x1024   ->    25 x  25 x  30\r\n   31 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\nLoaded: 4.249515 seconds\r\nRegion Avg IOU: 0.009045, Class: 1.000000, Obj: 0.619969, No Obj: 0.504332, Avg Recall: 0.000000,  count: 33\r\n\r\n\r\n\r\nhere is my cfg file\r\n[net]\r\n# Testing\r\nbatch=64\r\nsubdivisions=8\r\n# Training\r\n# batch=64\r\n# subdivisions=8\r\nheight=806\r\nwidth=806\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=0.001\r\nburn_in=1000\r\nmax_batches = 80200\r\npolicy=steps\r\nsteps=40000,60000\r\nscales=.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[convolutional]\r\nbatch_normalize=1\r\nsize=1\r\nstride=1\r\npad=1\r\nfilters=64\r\nactivation=leaky\r\n\r\n[reorg]\r\nstride=2\r\n\r\n[route]\r\nlayers=-1,-4\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\r\n[region]\r\nanchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071\r\nbias_match=1\r\nclasses=1\r\ncoords=4\r\nnum=5\r\nsoftmax=1\r\njitter=.3\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"},"comment":{"url":"https://api.github.com/repos/pjreddie/darknet/issues/comments/353774273","html_url":"https://github.com/pjreddie/darknet/issues/377#issuecomment-353774273","issue_url":"https://api.github.com/repos/pjreddie/darknet/issues/377","id":353774273,"user":{"login":"SteveIb","id":24913534,"avatar_url":"https://avatars2.githubusercontent.com/u/24913534?v=4","gravatar_id":"","url":"https://api.github.com/users/SteveIb","html_url":"https://github.com/SteveIb","followers_url":"https://api.github.com/users/SteveIb/followers","following_url":"https://api.github.com/users/SteveIb/following{/other_user}","gists_url":"https://api.github.com/users/SteveIb/gists{/gist_id}","starred_url":"https://api.github.com/users/SteveIb/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/SteveIb/subscriptions","organizations_url":"https://api.github.com/users/SteveIb/orgs","repos_url":"https://api.github.com/users/SteveIb/repos","events_url":"https://api.github.com/users/SteveIb/events{/privacy}","received_events_url":"https://api.github.com/users/SteveIb/received_events","type":"User","site_admin":false},"created_at":"2017-12-24T09:17:26Z","updated_at":"2017-12-24T09:17:26Z","author_association":"NONE","body":"another one\r\n![222](https://user-images.githubusercontent.com/24913534/34325502-f7014496-e89b-11e7-98d9-72e04aa76649.png)\r\n"}}
{"actor":{"display_login":"SteveIb"}}