diff --git a/python-sdk/nuscenes/eval/detection/README.md b/python-sdk/nuscenes/eval/detection/README.md index a9ca9dec..0cafbdfe 100644 --- a/python-sdk/nuscenes/eval/detection/README.md +++ b/python-sdk/nuscenes/eval/detection/README.md @@ -17,8 +17,8 @@ The goal of this task is to place a 3D bounding box around 10 different object c as well as estimating a set of attributes and the current velocity vector. ## Participation -The nuScenes detection [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/356) is open all year round for submission. -To participate in the challenge, please create an account at [EvalAI](http://evalai.cloudcv.org/web/challenges/challenge-page/356). +The nuScenes detection [evaluation server](https://eval.ai/web/challenges/challenge-page/356/overview) is open all year round for submission. +To participate in the challenge, please create an account at [EvalAI](https://eval.ai/web/challenges/challenge-page/356/overview). Then upload your zipped result file including all of the required [meta data](#results-format). After each challenge, the results will be exported to the nuScenes [leaderboard](https://www.nuscenes.org/object-detection) shown above. This is the only way to benchmark your method against the test dataset. @@ -29,26 +29,26 @@ Additionally we organize a number of challenges at leading Computer Vision confe Users that submit their results during the challenge period are eligible for awards. Any user that cannot attend the workshop (direct or via a representative) will be excluded from the challenge, but will still be listed on the leaderboard. -Click [here](http://evalai.cloudcv.org/web/challenges/challenge-page/356) for the **EvalAI detection evaluation server**. +Click [here](https://eval.ai/web/challenges/challenge-page/356/overview) for the **EvalAI detection evaluation server**. ### 5th AI Driving Olympics, NeurIPS 2020 The third nuScenes detection challenge will be held at [NeurIPS 2020](https://nips.cc/Conferences/2020/). Submission will open on Nov 15, 2020 and close in early Dec, 2020. Results and winners will be announced at the [5th AI Driving Olympics](https://driving-olympics.ai/) at NeurIPS 2020. -Note that this challenge uses the same [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/356) as previous detection challenges. +Note that this challenge uses the same [evaluation server](https://eval.ai/web/challenges/challenge-page/356/overview) as previous detection challenges. ### Workshop on Benchmarking Progress in Autonomous Driving, ICRA 2020 The second nuScenes detection challenge will be held at [ICRA 2020](https://www.icra2020.org/). The submission period will open April 1 and continue until May 28th, 2020. Results and winners will be announced at the [Workshop on Benchmarking Progress in Autonomous Driving](http://montrealrobotics.ca/driving-benchmarks/). -Note that the previous [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/356) can still be used to benchmark your results after the challenge period. +Note that the previous [evaluation server](https://eval.ai/web/challenges/challenge-page/356/overview) can still be used to benchmark your results after the challenge period. ### Workshop on Autonomous Driving, CVPR 2019 The first nuScenes detection challenge was held at CVPR 2019. Submission opened May 6 and closed June 12, 2019. Results and winners were announced at the Workshop on Autonomous Driving ([WAD](https://sites.google.com/view/wad2019)) at [CVPR 2019](http://cvpr2019.thecvf.com/). For more information see the [leaderboard](https://www.nuscenes.org/object-detection). -Note that the [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/356) can still be used to benchmark your results. +Note that the [evaluation server](https://eval.ai/web/challenges/challenge-page/356/overview) can still be used to benchmark your results. ## Submission rules ### Detection-specific rules diff --git a/python-sdk/nuscenes/eval/lidarseg/README.md b/python-sdk/nuscenes/eval/lidarseg/README.md index 9a9e11e1..b5606855 100644 --- a/python-sdk/nuscenes/eval/lidarseg/README.md +++ b/python-sdk/nuscenes/eval/lidarseg/README.md @@ -16,8 +16,8 @@ Here we define the lidar segmentation task on nuScenes. The goal of this task is to predict the category of every point in a set of point clouds. There are 16 categories (10 foreground classes and 6 background classes). ## Participation -The nuScenes lidarseg segmentation [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/) will be coming soon. -To participate in the challenge, please create an account at [EvalAI](http://evalai.cloudcv.org). +The nuScenes lidarseg segmentation [evaluation server](https://eval.ai) will be coming soon. +To participate in the challenge, please create an account at [EvalAI](https://eval.ai). Then upload your zipped result folder with the required [content](#results-format). After each challenge, the results will be exported to the nuScenes [leaderboard](https://www.nuscenes.org/lidar-segmentation) (coming soon). This is the only way to benchmark your method against the test dataset. @@ -28,14 +28,14 @@ Additionally we organize a number of challenges at leading Computer Vision confe Users that submit their results during the challenge period are eligible for awards. Any user that cannot attend the workshop (direct or via a representative) will be excluded from the challenge, but will still be listed on the leaderboard. -Click [here](http://evalai.cloudcv.org/web/challenges/challenge-page/) for the **EvalAI lidar segmentation evaluation server** (coming soon). +Click [here](https://eval.ai) for the **EvalAI lidar segmentation evaluation server** (coming soon). ### 5th AI Driving Olympics, NeurIPS 2020 The first nuScenes lidar segmentation challenge will be held at [NeurIPS 2020](https://nips.cc/Conferences/2020/). Submission will open on Nov 15, 2020 and close in early Dec, 2020. Results and winners will be announced at the [5th AI Driving Olympics](https://driving-olympics.ai/) at NeurIPS 2020. For more information see the [leaderboard](https://www.nuscenes.org/lidar-segmentation) (coming soon). -Note that the [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/) (coming soon) can still be used to benchmark your results. +Note that the [evaluation server](https://eval.ai) (coming soon) can still be used to benchmark your results. ## Submission rules ### Lidar segmentation-specific rules diff --git a/python-sdk/nuscenes/eval/prediction/README.md b/python-sdk/nuscenes/eval/prediction/README.md index c8709dd3..3616e946 100644 --- a/python-sdk/nuscenes/eval/prediction/README.md +++ b/python-sdk/nuscenes/eval/prediction/README.md @@ -14,8 +14,8 @@ A trajectory is a sequence of x-y locations. For this challenge, the predictions 2 hertz. ## Participation -The nuScenes prediction [evaluation server](http://evalai.cloudcv.org/web/challenges/challenge-page/591) is open all year round for submission. -To participate in the challenge, please create an account at [EvalAI](http://evalai.cloudcv.org/web/challenges/challenge-page/591). +The nuScenes prediction [evaluation server](https://eval.ai/web/challenges/challenge-page/591/overview) is open all year round for submission. +To participate in the challenge, please create an account at [EvalAI](https://eval.ai/web/challenges/challenge-page/591/overview). Then upload your zipped result file including all of the required [meta data](#results-format). After each challenge, the results will be exported to the nuScenes [leaderboard](https://www.nuscenes.org/prediction) shown above. This is the only way to benchmark your method against the test dataset. @@ -25,7 +25,7 @@ To allow users to benchmark the performance of their method against the communit Additionally, we intend to organize a number of challenges at leading Computer Vision and Machine Learning conference workshops. Users that submit their results during the challenge period are eligible for awards. These awards may be different for each challenge. -Click [here](http://evalai.cloudcv.org/web/challenges/challenge-page/591) for the **EvalAI prediction evaluation server**. +Click [here](https://eval.ai/web/challenges/challenge-page/591/overview) for the **EvalAI prediction evaluation server**. ### Workshop on Benchmarking Progress in Autonomous Driving, ICRA 2020 The first nuScenes prediction challenge will be held at [ICRA 2020](https://www.icra2020.org/). diff --git a/python-sdk/nuscenes/eval/tracking/README.md b/python-sdk/nuscenes/eval/tracking/README.md index 6494f402..b0efd677 100644 --- a/python-sdk/nuscenes/eval/tracking/README.md +++ b/python-sdk/nuscenes/eval/tracking/README.md @@ -38,7 +38,7 @@ In `loaders.py` we provide some methods to organize the raw box data into tracks ## Participation The nuScenes tracking evaluation server is open all year round for submission. -To participate in the challenge, please create an account at [EvalAI](http://evalai.cloudcv.org/web/challenges/challenge-page/475). +To participate in the challenge, please create an account at [EvalAI](https://eval.ai/web/challenges/challenge-page/476/overview). Then upload your zipped result file including all of the required [meta data](#results-format). The results will be exported to the nuScenes leaderboard shown above (coming soon). This is the only way to benchmark your method against the test dataset. @@ -49,7 +49,7 @@ Additionally we organize a number of challenges at leading Computer Vision confe Users that submit their results during the challenge period are eligible for awards. Any user that cannot attend the workshop (direct or via a representative) will be excluded from the challenge, but will still be listed on the leaderboard. -Click [here](http://evalai.cloudcv.org/web/challenges/challenge-page/475) for the **EvalAI tracking evaluation server**. +Click [here](https://eval.ai/web/challenges/challenge-page/476/overview) for the **EvalAI tracking evaluation server**. ### AI Driving Olympics (AIDO), NIPS 2019 The first nuScenes tracking challenge will be held at NIPS 2019.