Based on my understanding of the dataset this looks like a Multilabel Classification Problem to me where for each image we have to accurately predict the species of that whales and dolphins .
\nBut in the train dataset a species can have multiple individual ids present .
\nEg Species \"beluga\" has following individual ids 0079a7854ab4 , 00c2feba7e03 , 00f58de6056c and so on .
\nSo how do we have to predict appropriate Individual_Id for a given species since a species can have multiple Individual Ids .
\nAlso please do correct my understanding if its wrong.
\nThanks
Based on my understanding of the dataset this looks like a Multilabel Classification Problem to me where for each image we have to accurately predict the species of that whales and dolphins .
\nBut in the train dataset a species can have multiple individual ids present .
\nEg Species \"beluga\" has following individual ids 0079a7854ab4 , 00c2feba7e03 , 00f58de6056c and so on .
\nSo how do we have to predict appropriate Individual_Id for a given species since a species can have multiple Individual Ids .
\nAlso please do correct my understanding if its wrong.
\nThanks
You only have to predict the individual ids and not the species. For eg, id 0079a7854ab4 refers to one individual beluga
@debarshichanda so this means that for any image we have to predict top 5 Individual Ids amongst 15587 Individual Ids , so the number of classes here are 15587 ?
", "rawMarkdown": "debarshichanda so this means that for any image we have to predict top 5 Individual Ids amongst 15587 Individual Ids , so the number of classes here are 15587 ?", "votes": null }, { "id": "1673947", "postDate": "02/03/2022 05:39:31", "content": "@sayedathar11 yes is right 🙂
", "rawMarkdown": "sayedathar11 yes is right 🙂", "votes": null }, { "id": "1673962", "postDate": "02/03/2022 05:52:11", "content": "and you can predict new_individual, which is to say a prediction that the individual is not represented in the train dataset. This mimics reality where an animal may (and often is) new to the research group
", "rawMarkdown": "and you can predict new_individual, which is to say a prediction that the individual is not represented in the train dataset. This mimics reality where an animal may (and often is) new to the research group", "votes": null }, { "id": "1674457", "postDate": "02/03/2022 14:23:30", "content": "There are many approaches to solve this task. What about multilabelling, I think we should try to do Multi-Labeling, which will be helpful to reduce the \"region\" of searching for similar whales. For example, we have 15000+ different whales, but there are only 100+ whales of some species, so we will search the similar whales there and the error will be decreased. Just my thoughts, if you have others, reply to me.
", "rawMarkdown": "There are many approaches to solve this task. What about multilabelling, I think we should try to do Multi-Labeling, which will be helpful to reduce the \"region\" of searching for similar whales. For example, we have 15000+ different whales, but there are only 100+ whales of some species, so we will search the similar whales there and the error will be decreased. Just my thoughts, if you have others, reply to me.", "votes": null }, { "id": "1674525", "postDate": "02/03/2022 15:11:56", "content": "one potential problem here is that some individuals may be easier to detect directly rather than labeling to species first. For example, fin, sei and bryde's whales all have very similar dorsal fins, where we can expect more variation between individuals than between species.
", "rawMarkdown": "one potential problem here is that some individuals may be easier to detect directly rather than labeling to species first. For example, fin, sei and bryde's whales all have very similar dorsal fins, where we can expect more variation between individuals than between species.", "votes": null }, { "id": "1674574", "postDate": "02/03/2022 15:50:51", "content": "Due to the nature of the problem, you may have more success (or just an easier time) looking at this like a clustering/similarity problem. (Kind of like the previous Shopee competition).
\nIn the previous competition, the HappyWhale team implemented a solution that leveraged ArcFace. This encourages us to think about the problem in a way that we might think about facial recognition or some other few-shot classification task.
\nMy basic approach would be:
\nTake all this with a grain of salt as I have barely begun to look into this competition, but I will be building a baseline along these lines over the next few days. Hope this helps!
", "rawMarkdown": "Due to the nature of the problem, you may have more success (or just an easier time) looking at this like a clustering/similarity problem. (Kind of like the previous Shopee competition).\n\nIn the previous competition, the HappyWhale team implemented a solution that leveraged [**ArcFace**](https://arxiv.org/abs/1801.07698). This encourages us to think about the problem in a way that we might think about facial recognition or some other few-shot classification task. \n\nYes, there are many problems that will be disclosed, but we should try it!
", "rawMarkdown": "Yes, there are many problems that will be disclosed, but we should try it!", "votes": null }, { "id": "1675431", "postDate": "02/04/2022 09:09:01", "content": "@dschettler8845 , Thanks for your comment , I actually didn't participated in Shoppe Competition so didn't thought of approach , also haven't look into details of previous Happywhale competition , but your reasoning does sounds logical to me . I will definitely learn this and try this approach out and see how it works .
", "rawMarkdown": "dschettler8845 , Thanks for your comment , I actually didn't participated in Shoppe Competition so didn't thought of approach , also haven't look into details of previous Happywhale competition , but your reasoning does sounds logical to me . I will definitely learn this and try this approach out and see how it works .", "votes": null } ], "comments": [ { "id": 1673928, "author_name": "debarshichanda", "author_url": "", "post_date": "02/03/2022 05:15:35", "content": "You only have to predict the individual ids and not the species. For eg, id 0079a7854ab4 refers to one individual beluga
@debarshichanda so this means that for any image we have to predict top 5 Individual Ids amongst 15587 Individual Ids , so the number of classes here are 15587 ?
", "votes": null, "replies": [] }, { "id": 1673947, "author_name": "rdizzl3", "author_url": "", "post_date": "02/03/2022 05:39:31", "content": "@sayedathar11 yes is right 🙂
", "votes": null, "replies": [] }, { "id": 1673962, "author_name": "tedcheese", "author_url": "", "post_date": "02/03/2022 05:52:11", "content": "and you can predict new_individual, which is to say a prediction that the individual is not represented in the train dataset. This mimics reality where an animal may (and often is) new to the research group
", "votes": null, "replies": [] } ] }, { "id": 1674457, "author_name": "vad13irt", "author_url": "", "post_date": "02/03/2022 14:23:30", "content": "There are many approaches to solve this task. What about multilabelling, I think we should try to do Multi-Labeling, which will be helpful to reduce the \"region\" of searching for similar whales. For example, we have 15000+ different whales, but there are only 100+ whales of some species, so we will search the similar whales there and the error will be decreased. Just my thoughts, if you have others, reply to me.
", "votes": null, "replies": [ { "id": 1674525, "author_name": "tedcheese", "author_url": "", "post_date": "02/03/2022 15:11:56", "content": "one potential problem here is that some individuals may be easier to detect directly rather than labeling to species first. For example, fin, sei and bryde's whales all have very similar dorsal fins, where we can expect more variation between individuals than between species.
", "votes": null, "replies": [] }, { "id": 1674603, "author_name": "vad13irt", "author_url": "", "post_date": "02/03/2022 16:20:30", "content": "Yes, there are many problems that will be disclosed, but we should try it!
", "votes": null, "replies": [] } ] }, { "id": 1674574, "author_name": "dschettler8845", "author_url": "", "post_date": "02/03/2022 15:50:51", "content": "Due to the nature of the problem, you may have more success (or just an easier time) looking at this like a clustering/similarity problem. (Kind of like the previous Shopee competition).
\nIn the previous competition, the HappyWhale team implemented a solution that leveraged ArcFace. This encourages us to think about the problem in a way that we might think about facial recognition or some other few-shot classification task.
\nMy basic approach would be:
\nTake all this with a grain of salt as I have barely begun to look into this competition, but I will be building a baseline along these lines over the next few days. Hope this helps!
", "votes": null, "replies": [ { "id": 1675431, "author_name": "sayedathar11", "author_url": "", "post_date": "02/04/2022 09:09:01", "content": "@dschettler8845 , Thanks for your comment , I actually didn't participated in Shoppe Competition so didn't thought of approach , also haven't look into details of previous Happywhale competition , but your reasoning does sounds logical to me . I will definitely learn this and try this approach out and see how it works .
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1673910": "Based on my understanding of the dataset this looks like a Multilabel Classification Problem to me where for each image we have to accurately predict the species of that whales and dolphins .\nBut in the train dataset a species can have multiple individual ids present .\nEg Species \"beluga\" has following individual ids 0079a7854ab4 , 00c2feba7e03 , 00f58de6056c and so on . \nSo how do we have to predict appropriate Individual_Id for a given species since a species can have multiple Individual Ids .\nAlso please do correct my understanding if its wrong.\nThanks", "1673928": "You only have to predict the individual ids and not the species. For eg, id `0079a7854ab4` refers to one individual beluga", "1673939": "debarshichanda so this means that for any image we have to predict top 5 Individual Ids amongst 15587 Individual Ids , so the number of classes here are 15587 ?", "1673947": "sayedathar11 yes is right 🙂", "1673962": "and you can predict new_individual, which is to say a prediction that the individual is not represented in the train dataset. This mimics reality where an animal may (and often is) new to the research group", "1674457": "There are many approaches to solve this task. What about multilabelling, I think we should try to do Multi-Labeling, which will be helpful to reduce the \"region\" of searching for similar whales. For example, we have 15000+ different whales, but there are only 100+ whales of some species, so we will search the similar whales there and the error will be decreased. Just my thoughts, if you have others, reply to me.", "1674525": "one potential problem here is that some individuals may be easier to detect directly rather than labeling to species first. For example, fin, sei and bryde's whales all have very similar dorsal fins, where we can expect more variation between individuals than between species.", "1674574": "Due to the nature of the problem, you may have more success (or just an easier time) looking at this like a clustering/similarity problem. (Kind of like the previous Shopee competition).\n\nIn the previous competition, the HappyWhale team implemented a solution that leveraged [**ArcFace**](https://arxiv.org/abs/1801.07698). This encourages us to think about the problem in a way that we might think about facial recognition or some other few-shot classification task. \n\n