payload: {"action":"created","issue":{"url":"https://api.github.com/repos/AlexeyAB/darknet/issues/1538","repository_url":"https://api.github.com/repos/AlexeyAB/darknet","labels_url":"https://api.github.com/repos/AlexeyAB/darknet/issues/1538/labels{/name}","comments_url":"https://api.github.com/repos/AlexeyAB/darknet/issues/1538/comments","events_url":"https://api.github.com/repos/AlexeyAB/darknet/issues/1538/events","html_url":"https://github.com/AlexeyAB/darknet/issues/1538","id":356924342,"node_id":"MDU6SXNzdWUzNTY5MjQzNDI=","number":1538,"title":"YOLO v3 training on stock exchanges graphics for pattern recognition","user":{"login":"xinger","id":3993459,"node_id":"MDQ6VXNlcjM5OTM0NTk=","avatar_url":"https://avatars1.githubusercontent.com/u/3993459?v=4","gravatar_id":"","url":"https://api.github.com/users/xinger","html_url":"https://github.com/xinger","followers_url":"https://api.github.com/users/xinger/followers","following_url":"https://api.github.com/users/xinger/following{/other_user}","gists_url":"https://api.github.com/users/xinger/gists{/gist_id}","starred_url":"https://api.github.com/users/xinger/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/xinger/subscriptions","organizations_url":"https://api.github.com/users/xinger/orgs","repos_url":"https://api.github.com/users/xinger/repos","events_url":"https://api.github.com/users/xinger/events{/privacy}","received_events_url":"https://api.github.com/users/xinger/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":11,"created_at":"2018-09-04T18:24:56Z","updated_at":"2019-04-13T14:10:45Z","closed_at":null,"author_association":"NONE","body":"Hi, first of all, thank you for detailed readme and job that you doing with this repo.\r\n\r\nI have a task for visual recognition of stock exchange figures (f.e. head and shoulders, not a crypto btw), and this task is so hard to resolve by math (for me), so, I decide to try YOLO v3.\r\n\r\nI managed to prepare a huge dataset (which continue to grown) with almost ±4000 of pictures. The dataset contains 11 patterns, every pattern contains 2 classes — first class will say about the buying process, the second one about the selling process — so, classes are mirrored. In total, I have 22 classes. \r\n\r\nI did a lot of experiments with different kinds of the dataset and .cfg file:\r\nI have begun from 1 class for test training and ended up with the full dataset for training with 4k pictures for tests. But, after 50k iterations I still have no any even close to satisfactory results. \r\n\r\nFor example, in this image:\r\n\r\n![gizxe6za](https://user-images.githubusercontent.com/3993459/45049657-ed1d6400-b087-11e8-95a3-b748be6c7ef1.png)\r\nYolo-detect recognized a photo part and whole graphic part, not patterns inside the graphic.\r\n\r\nIf I’ll run the same command with -thresh 0, this will be a result:\r\n\r\n![q8eonufg](https://user-images.githubusercontent.com/3993459/45049686-0aeac900-b088-11e8-96e1-c910fff08e6e.png)\r\n\r\nSo, classes exist in weight files. I also checked every 5k iteration weight file, and still no good looking result, I don’t think it could be an “over-training” problem.\r\n\r\nNow, few details about the dataset itself:\r\nPatterns are manually prepared and stored in DB, so I can export them with any visual style or data format. \r\n\r\nI decided, that for the neural network it will be easier if I will prepare the dataset in style, where only 4 colors and every candle is 1-pixel width. For example, on this picture, in the middle you can see pattern itself:\r\n\r\n![zlwljlzg](https://user-images.githubusercontent.com/3993459/45049728-2b1a8800-b088-11e8-8d5f-a60fe6727898.png)\r\n\r\nThis is the example of «pure» pattern, without any marked data on it, as you see, they are looking pretty close to each other:\r\n![1lnjctrw](https://user-images.githubusercontent.com/3993459/45049780-5b622680-b088-11e8-9b66-797c182780c9.png)\r\nHere is my final [config file](https://www.dropbox.com/s/ly2uhfg2q4kcusw/yolov3-22-class-v7.cfg?dl=0)\r\nAs you wrote at your _readme.md_, I also changed strings in .cfg about layers = -1, 11 and stride=4, because of patterns, they can be pretty small.\r\n\r\n**Why, I think, it isn’t working for me:**\r\n1) I run training on the base weight file darknet53.conv.74, that is could be strange because this weight was prepared for photos?\r\n\r\n2) I think I need dramatically change my .cfg file because it was prepared for “photo recognition”, not “graphic patterns recognition”\r\n\r\n3) Maybe, something wrong in pictures style that I feeding to YOLO?\r\n\r\nCould you please share any thoughts about my issue? \r\nI’m desperate and looking for any advice 🙂"},"comment":{"url":"https://api.github.com/repos/AlexeyAB/darknet/issues/comments/482812478","html_url":"https://github.com/AlexeyAB/darknet/issues/1538#issuecomment-482812478","issue_url":"https://api.github.com/repos/AlexeyAB/darknet/issues/1538","id":482812478,"node_id":"MDEyOklzc3VlQ29tbWVudDQ4MjgxMjQ3OA==","user":{"login":"AlexeyAB","id":4096485,"node_id":"MDQ6VXNlcjQwOTY0ODU=","avatar_url":"https://avatars3.githubusercontent.com/u/4096485?v=4","gravatar_id":"","url":"https://api.github.com/users/AlexeyAB","html_url":"https://github.com/AlexeyAB","followers_url":"https://api.github.com/users/AlexeyAB/followers","following_url":"https://api.github.com/users/AlexeyAB/following{/other_user}","gists_url":"https://api.github.com/users/AlexeyAB/gists{/gist_id}","starred_url":"https://api.github.com/users/AlexeyAB/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/AlexeyAB/subscriptions","organizations_url":"https://api.github.com/users/AlexeyAB/orgs","repos_url":"https://api.github.com/users/AlexeyAB/repos","events_url":"https://api.github.com/users/AlexeyAB/events{/privacy}","received_events_url":"https://api.github.com/users/AlexeyAB/received_events","type":"User","site_admin":false},"created_at":"2019-04-13T14:10:45Z","updated_at":"2019-04-13T14:10:45Z","author_association":"OWNER","body":"@xinger Hi,\r\n\r\nSo network think that the pattern is there.\r\n\r\n* if the most of detected objects with shifted coords are False Detection (FP), then just reject such detections if(shift > some_pixels)\r\n\r\n* if the most of detected objects with shifted coords are True Detection (TP), then just force change the coordinate to required in your code"}}
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