![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F505538%2F416961869a7be93dcc241afc243a82aa%2FDETR.png?generation=1591647223902151&alt=media\")
PyTorch training code and pre-trained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch.
\n\n
DETR
\n\nWhat it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.
\n\nGithub Code-\nDETR
\n\nPaper -\nhttps://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers
\n\nEnjoy working with Object Detection Models - MaskRCNN/Yolov4/DETR.
", "messages": [ { "id": 878768, "postDate": "2020-06-08T20:17:41.197Z", "content": "PyTorch training code and pre-trained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch.
\n\n
DETR
\n\nWhat it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.
\n\nGithub Code-\nDETR
\n\nPaper -\nhttps://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers
\n\nEnjoy working with Object Detection Models - MaskRCNN/Yolov4/DETR.
", "rawMarkdown": "PyTorch training code and pre-trained models for DETR (DEtection TRansformer). **We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch.**\n\n\n\nDETR\n\nWhat it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.\n\nGithub Code-\n[DETR](https://github.com/facebookresearch/detr)\n\nPaper -\nhttps://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers\n\nEnjoy working with Object Detection Models - MaskRCNN/Yolov4/DETR.", "votes": 2 }, { "id": 878769, "postDate": "2020-06-08T20:19:55.043Z", "content": "Been posted here twice already. I don't think it's very viable due to the computational requirements.
", "rawMarkdown": "Been posted here twice already. I don't think it's very viable due to the computational requirements.", "replies": [ { "id": 878774, "postDate": "2020-06-08T20:28:33.543Z", "content": "As FB Article says this model will take time to mature to detect small objects. I think some of the researcher will optimise the Code to reduce computational requirement. I lile the idea in taking Transformer into Object Detection. \nAnyways, I have not seen this here so posted it. Kaggle should run a model to check similarity of topic before submit. π
", "rawMarkdown": "As FB Article says this model will take time to mature to detect small objects. I think some of the researcher will optimise the Code to reduce computational requirement. I lile the idea in taking Transformer into Object Detection. \nAnyways, I have not seen this here so posted it. Kaggle should run a model to check similarity of topic before submit. π ", "votes": -2 }, { "id": 878789, "postDate": "2020-06-08T20:56:23.197Z", "content": "You should check similarity of topic before submit. π
\n\nNevertheless, the progress with transformers is astonishing and I am very interested in the results in the upcoming months
", "rawMarkdown": "**You** should check similarity of topic before submit. π\n\nNevertheless, the progress with transformers is astonishing and I am very interested in the results in the upcoming months", "votes": 1 } ] }, { "id": 1033978, "postDate": "2020-10-01T11:57:57.553Z", "rawMarkdown": "", "isDeleted": true } ], "comments": [ { "id": 878769, "author_name": "Matt", "author_url": "", "post_date": "2020-06-08T20:19:55.043000", "content": "Been posted here twice already. I don't think it's very viable due to the computational requirements.
", "votes": 0, "replies": [ { "id": 878774, "author_name": "SumitJha", "author_url": "", "post_date": "2020-06-08T20:28:33.543000", "content": "As FB Article says this model will take time to mature to detect small objects. I think some of the researcher will optimise the Code to reduce computational requirement. I lile the idea in taking Transformer into Object Detection. \nAnyways, I have not seen this here so posted it. Kaggle should run a model to check similarity of topic before submit. π
", "votes": -2, "replies": [] }, { "id": 878789, "author_name": "Pascal Pfeiffer", "author_url": "", "post_date": "2020-06-08T20:56:23.197000", "content": "You should check similarity of topic before submit. π
\n\nNevertheless, the progress with transformers is astonishing and I am very interested in the results in the upcoming months
", "votes": 1, "replies": [] } ] }, { "id": 1033978, "author_name": "", "author_url": "", "post_date": "2020-10-01T11:57:57.553000", "content": "", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "878768": "PyTorch training code and pre-trained models for DETR (DEtection TRansformer). **We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch.**\n\n\n\nDETR\n\nWhat it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.\n\nGithub Code-\n[DETR](https://github.com/facebookresearch/detr)\n\nPaper -\nhttps://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers\n\nEnjoy working with Object Detection Models - MaskRCNN/Yolov4/DETR.", "878769": "Been posted here twice already. I don't think it's very viable due to the computational requirements.", "1033978": "" } }