@@ -16,11 +16,12 @@ After a long time of industry practice polishing, PaddleDetection has had smooth
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@@ -16,11 +16,12 @@ After a long time of industry practice polishing, PaddleDetection has had smooth
### Product dynamic
### Product dynamic
- 2020.11.20: Release `release/0.5` version, Please refer to [change log](docs/CHANGELOG.md) for details.
- 2020.11.10: Added [SOLOv2](configs/solov2) as an instance segmentation model, which reached 38.6 FPS on a single Tesla V100, 38.8 mask AP on Coco-Val dataset, and inference speed increased by 24% and mAP by 2.4 percentage points.
- 2020.10.30: PP-YOLO support rectangular image input, and add a new PACT quantization strategy for slim。
- 2020.10.30: PP-YOLO support rectangular image input, and add a new PACT quantization strategy for slim。
- 2020.10.01: Added SOLOv2 as an instance segmentation model, which reached 38.6 FPS on a single Tesla V100, 38.8 mask AP on Coco-Val dataset, and inference speed increased by 24% and mAP by 2.4 percentage points.
- 2020.09.30: Released the [mobile-side detection demo](deploy/android_demo), and you can directly scan the code for installation experience.
- 2020.09.30: Released the mobile-side detection demo, and you can directly scan the code for installation experience.
- 2020.09.21-27: [Object detection 7 days of punching class] Hand in hand to teach you from the beginning to the advanced level, in-depth understanding of the object detection algorithm life. Join the course QQ group (1136406895) to study together :)
- 2020.09.21-27: [Object detection 7 days of punching class] Hand in hand to teach you from the beginning to the advanced level, in-depth understanding of the object detection algorithm life. Join the course QQ group (1136406895) to study together :)
- 2020.07.24: [PP-YOLO](https://arxiv.org/abs/2007.12099), which is **the most practical** object detection model, was released, it deeply considers the double demands of industrial applications for accuracy and speed, and reached accuracy as 45.2% (the latest 45.9%) on COCO dataset, inference speed as 72.9 FPS on a single Test V100. Please refer to [PP-YOLO](https://arxiv.org/abs/2007.12099) for details.
- 2020.07.24: [PP-YOLO](https://arxiv.org/abs/2007.12099), which is **the most practical** object detection model, was released, it deeply considers the double demands of industrial applications for accuracy and speed, and reached accuracy as 45.2% (the latest 45.9%) on COCO dataset, inference speed as 72.9 FPS on a single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
- 2020.06.11: Publish 676 classes of large-scale server-side practical object detection models that are applicable to most application scenarios and can be used directly for prediction or for fine-tuning other tasks.
- 2020.06.11: Publish 676 classes of large-scale server-side practical object detection models that are applicable to most application scenarios and can be used directly for prediction or for fine-tuning other tasks.
