{"action":"created","issue":{"url":"https://api.github.com/repos/weiliu89/caffe/issues/34","repository_url":"https://api.github.com/repos/weiliu89/caffe","labels_url":"https://api.github.com/repos/weiliu89/caffe/issues/34/labels{/name}","comments_url":"https://api.github.com/repos/weiliu89/caffe/issues/34/comments","events_url":"https://api.github.com/repos/weiliu89/caffe/issues/34/events","html_url":"https://github.com/weiliu89/caffe/issues/34","id":165106458,"number":34,"title":"new base network trials","user":{"login":"youngwanLEE","id":13238578,"avatar_url":"https://avatars.githubusercontent.com/u/13238578?v=3","gravatar_id":"","url":"https://api.github.com/users/youngwanLEE","html_url":"https://github.com/youngwanLEE","followers_url":"https://api.github.com/users/youngwanLEE/followers","following_url":"https://api.github.com/users/youngwanLEE/following{/other_user}","gists_url":"https://api.github.com/users/youngwanLEE/gists{/gist_id}","starred_url":"https://api.github.com/users/youngwanLEE/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/youngwanLEE/subscriptions","organizations_url":"https://api.github.com/users/youngwanLEE/orgs","repos_url":"https://api.github.com/users/youngwanLEE/repos","events_url":"https://api.github.com/users/youngwanLEE/events{/privacy}","received_events_url":"https://api.github.com/users/youngwanLEE/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2016-07-12T15:16:35Z","updated_at":"2016-07-13T06:24:39Z","closed_at":null,"body":"Thanks in advance for your wonderful code :)\r\n\r\nRecently, I tried to change the VGG-16 base network to ResNet-50L & ResNet-101L by using pretrained model from K. He Github repo. and reffering your code 'ResBody' and 'Res152Body' in model_libs.py.\r\n\r\nIn ResNet-50L version, I used res3d_branch2c and res4f_branch2c for mbox_source_layers.\r\nIn ResNet-101L version, I used res3b3_branch2c and res4b22_branch2c for mbox_source_layers.\r\n\r\nIn addition, I also freezed relatively previous layers.\r\n\r\nHowever, the results on ResNet50 & ResNet101 always were not better than VGG-base.\r\n\r\nI read in previous Issues that you already successfully tested Inception-v3 and ResNet101L. I wonder your results.\r\n\r\nAnd Could you uploaded your pretrained model on Inception-v3 and ResNet101L ?\r\n\r\nThank you."},"comment":{"url":"https://api.github.com/repos/weiliu89/caffe/issues/comments/232267357","html_url":"https://github.com/weiliu89/caffe/issues/34#issuecomment-232267357","issue_url":"https://api.github.com/repos/weiliu89/caffe/issues/34","id":232267357,"user":{"login":"youngwanLEE","id":13238578,"avatar_url":"https://avatars.githubusercontent.com/u/13238578?v=3","gravatar_id":"","url":"https://api.github.com/users/youngwanLEE","html_url":"https://github.com/youngwanLEE","followers_url":"https://api.github.com/users/youngwanLEE/followers","following_url":"https://api.github.com/users/youngwanLEE/following{/other_user}","gists_url":"https://api.github.com/users/youngwanLEE/gists{/gist_id}","starred_url":"https://api.github.com/users/youngwanLEE/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/youngwanLEE/subscriptions","organizations_url":"https://api.github.com/users/youngwanLEE/orgs","repos_url":"https://api.github.com/users/youngwanLEE/repos","events_url":"https://api.github.com/users/youngwanLEE/events{/privacy}","received_events_url":"https://api.github.com/users/youngwanLEE/received_events","type":"User","site_admin":false},"created_at":"2016-07-13T06:24:39Z","updated_at":"2016-07-13T06:24:39Z","body":"Actually, I finetuned using KITTI dataset for road object detection.\r\n\r\nThe test results were shown as below : \r\nval_acc : VGG - 0.39 | Res101 - 0.34 | Res50 - 0.33\r\n\r\nI have another issue : processing time\r\n\r\nI experimented processing times of various version using KITTI test dataset : 2494 images\r\n\r\nI tried to use two metrics : 1) python script reffered to ipynb  2)./build/tools/caffe time  \r\n\r\n-------------------------------------------------------------------------------------------------------------------\r\n1) python script\r\n\r\nI reffered to your ssd_detect.ipynb and time check like this : \r\n\r\nfor s in image_list :\r\n...\r\n...\r\n    # Forward pass.\r\n    start = time.time()\r\n    detections = net.forward()['detection_out']\r\n    end = time.time() - start\r\n    sumPTime += end\r\n    ....\r\n    ...\r\navgPTime = sumPTime/cntFrame\r\n\r\nWith this metric, The results are :\r\n\r\nOn 980 Ti,        VGG - 22ms | Res50 - 32ms\r\nOn TX1 board, VGG-  1.01s | Res50 - 336ms\r\n\r\n\r\n-------------------------------------------------------------------------------------------------------------------\r\n\r\n2) caffe time --model = deploy.prototxt\r\n\r\nOn 980 Ti,        VGG - 22.75ms | Res50 - 24.72ms\r\nOn TX1 board, VGG-  262.931ms | Res50 - 229.372ms\r\n\r\n\r\n-------------------------------------------------------------------------------------------------------------------\r\n\r\nAs you can see, on TX1 board experimental results are strange. \r\n\r\nVGG result using python script on TX1 is very slow whereas on 980Ti result  is normal. \r\n\r\nQ1. \r\nI wonder why VGG version on TX1 board is slower than Res50L one.\r\n\r\nQ2.\r\nIn addition, without test images 'caffe time command' measured the whole network before detection_output layer which computed NMS.\r\n\r\nfor the exact time-check, when using 'caffe time command',  should we have included detection_output layers ? \r\n\r\nActually, I tried to caffe time command with --model=test.prototxt, But,\r\n\r\nUnknown bottom blob 'data' (layer 'conv1', bottom index 0)\r\n*** Check failure stack trace: ***\r\n\r\nthis error message occurred because of data layer.\r\n\r\nIn conclusion,  I want to know exactly evaluate FPS(or processing time ) on the SSD framework. \r\n\r\nThank you very much\r\n\r\n\r\n\r\n"}}
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