From 7411827ffcf02c44ec08f790cec71cceff6f37a0 Mon Sep 17 00:00:00 2001 From: WongGawa Date: Thu, 7 Nov 2024 11:08:20 +0800 Subject: [PATCH] update readme for mindspore version of 2.3.1 --- GETTING_STARTED.md | 2 +- README.md | 14 +++---- configs/yolov3/README.md | 54 +++++++++++++------------ configs/yolov4/README.md | 51 ++++++++++++------------ configs/yolov5/README.md | 63 ++++++++++++++--------------- configs/yolov7/README.md | 59 ++++++++++++++-------------- configs/yolov8/README.md | 85 +++++++++++++++++++++------------------- configs/yolox/README.md | 63 ++++++++++++++--------------- 8 files changed, 201 insertions(+), 190 deletions(-) diff --git a/GETTING_STARTED.md b/GETTING_STARTED.md index eeb3563d..a80ba9cc 100644 --- a/GETTING_STARTED.md +++ b/GETTING_STARTED.md @@ -29,7 +29,7 @@ to understand their behavior. Some common arguments are: * Prepare your dataset in YOLO format. If trained with COCO (YOLO format), prepare it from [yolov5](https://github.com/ultralytics/yolov5) or the darknet.
- + View More ``` coco/ {train,val}2017.txt diff --git a/README.md b/README.md index 3fbb09fb..12a2efee 100644 --- a/README.md +++ b/README.md @@ -14,13 +14,13 @@ MindYOLO implements state-of-the-art YOLO series algorithms based on MindSpore. The following is the corresponding `mindyolo` versions and supported `mindspore` versions. -| mindyolo | mindspore | -| :--: | :--: | -| master | master | -| 0.4 | 2.3.0 | -| 0.3 | 2.2.10 | -| 0.2 | 2.0 | -| 0.1 | 1.8 | +| mindyolo | mindspore | +| :------: | :---------: | +| master | master | +| 0.4 | 2.3.0/2.3.1 | +| 0.3 | 2.2.10 | +| 0.2 | 2.0 | +| 0.1 | 1.8 | diff --git a/configs/yolov3/README.md b/configs/yolov3/README.md index ff4959bf..ae327763 100644 --- a/configs/yolov3/README.md +++ b/configs/yolov3/README.md @@ -9,30 +9,11 @@ We present some updates to YOLO! We made a bunch of little design changes to mak -## Results +## Requirements -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | -| YOLOv3 | Darknet53 | 16 * 8 | 640 | MS COCO 2017 | 45.5 | 61.9M | [yaml](./yolov3.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-adfb27af.ckpt) | -
- -
-performance tested on Ascend 910*(8p) - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOv3 | Darknet53 | 16 * 8 | 640 | MS COCO 2017 | 46.6 | 396.60 | 61.9M | [yaml](./yolov3.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-81895f09-910v2.ckpt) | -
- -
- -#### Notes - -- Box mAP: Accuracy reported on the validation set. -- We referred to a commonly used third-party [YOLOv3](https://github.com/ultralytics/yolov3) implementation. +| mindspore | ascend driver | firmware | cann toolkit/kernel +| :-------: | :-----------: | :----------: | :----------------: +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 ## Quick Start @@ -40,7 +21,8 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/ ### Training -
+
+View More #### - Pretraining Model @@ -85,9 +67,29 @@ To validate the accuracy of the trained model, you can use `test.py` and parse t python test.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` -### Deployment +## Performance + +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode + +| model name | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv3 | 8 | 16 | 640x640 | O2 | 274.32s | 383.68 | 333.61 | 46.6% | [yaml](./yolov3.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-81895f09-910v2.ckpt) | + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model name | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv3 | 8 | 16 | 640x640 | O2 | 160.80s | 409.66 | 312.45 | 45.5% | [yaml](./yolov3.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-adfb27af.ckpt) | + + +
+ +### Notes + +- Box mAP: Accuracy reported on the validation set. +- We referred to a commonly used third-party [YOLOv3](https://github.com/ultralytics/yolov3) implementation. -See [here](../../deploy/README.md). ## References diff --git a/configs/yolov4/README.md b/configs/yolov4/README.md index d9255029..568a3147 100644 --- a/configs/yolov4/README.md +++ b/configs/yolov4/README.md @@ -23,30 +23,11 @@ AP (65.7% AP50) for the MS COCO dataset at a realtime speed of 65 FPS on Tesla V -## Results +## Requirements -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | -| YOLOv4 | CSPDarknet53 | 16 * 8 | 608 | MS COCO 2017 | 45.4 | 27.6M | [yaml](./yolov4.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-50172f93.ckpt) | -| YOLOv4 | CSPDarknet53(silu) | 16 * 8 | 608 | MS COCO 2017 | 45.8 | 27.6M | [yaml](./yolov4-silu.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_silu_320e_map458-bdfc3205.ckpt) | -
- -
-performance tested on Ascend 910*(8p) - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOv4 | CSPDarknet53 | 16 * 8 | 608 | MS COCO 2017 | 46.1 | 337.25 | 27.6M | [yaml](./yolov4.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-64b8506f-910v2.ckpt) | -
- -
- -#### Notes - -- Box mAP: Accuracy reported on the validation set. +| mindspore | ascend driver | firmware | cann toolkit/kernel +| :-------: | :-----------: | :----------: | :----------------: +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 ## Quick Start @@ -55,6 +36,7 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/ ### Training
+View More #### - Pretraining Model @@ -104,9 +86,28 @@ To validate the accuracy of the trained model, you can use `test.py` and parse t python test.py --config ./configs/yolov4/yolov4-silu.yaml --device_target Ascend --iou_thres 0.6 --weight /PATH/TO/WEIGHT.ckpt ``` -### Deployment +## Performance -See [here](../../deploy/README.md). +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode + +| model name | backbone | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv4 | CSPDarknet53 | 8 | 16 | 608x608 | O2 | 467.47s | 308.43 | 415.01 | 46.1% | [yaml](./yolov4.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-64b8506f-910v2.ckpt) | + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model name | backbone | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv4 | CSPDarknet53 | 8 | 16 | 608x608 | O2 | 188.52s | 505.98 | 252.97 | 45.4% | [yaml](./yolov4.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-50172f93.ckpt) | +| YOLOv4 | CSPDarknet53(silu) | 8 | 16 | 608x608 | O2 | 274.18s | 443.21 | 288.80 | 45.8% | [yaml](./yolov4-silu.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_silu_320e_map458-bdfc3205.ckpt) | + + +
+ +### Notes + +- Box mAP: Accuracy reported on the validation set. ## References diff --git a/configs/yolov5/README.md b/configs/yolov5/README.md index a80b2589..282d1a12 100644 --- a/configs/yolov5/README.md +++ b/configs/yolov5/README.md @@ -6,36 +6,11 @@ YOLOv5 is a family of object detection architectures and models pretrained on th -## Results - -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | -| YOLOv5 | N | 32 * 8 | 640 | MS COCO 2017 | 27.3 | 1.9M | [yaml](./yolov5n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt) | -| YOLOv5 | S | 32 * 8 | 640 | MS COCO 2017 | 37.6 | 7.2M | [yaml](./yolov5s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt) | -| YOLOv5 | M | 32 * 8 | 640 | MS COCO 2017 | 44.9 | 21.2M | [yaml](./yolov5m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt) | -| YOLOv5 | L | 32 * 8 | 640 | MS COCO 2017 | 48.5 | 46.5M | [yaml](./yolov5l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt) | -| YOLOv5 | X | 16 * 8 | 640 | MS COCO 2017 | 50.5 | 86.7M | [yaml](./yolov5x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5x_300e_mAP505-97d36ddc.ckpt) | -
- -
-performance tested on Ascend 910*(8p) - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOv5 | N | 32 * 8 | 640 | MS COCO 2017 | 27.4 | 736.08 | 1.9M | [yaml](./yolov5n.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-bedf9a93-910v2.ckpt) | -| YOLOv5 | S | 32 * 8 | 640 | MS COCO 2017 | 37.6 | 787.34 | 7.2M | [yaml](./yolov5s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-df4a45b6-910v2.ckpt) | -
- -
+## Requirements -#### Notes - -- Box mAP: Accuracy reported on the validation set. -- We refer to the official [YOLOV5](https://github.com/ultralytics/yolov5) to reproduce the P5 series model, and the differences are as follows: - 1. We use 8x NPU(Ascend910) for training, and the single-NPU batch size is 32. This is different from the official code. +| mindspore | ascend driver | firmware | cann toolkit/kernel +| :-------: | :-----------: | :----------: | :----------------: +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 ## Quick Start @@ -44,6 +19,7 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/ ### Training
+View More #### - Distributed Training @@ -79,9 +55,34 @@ To validate the accuracy of the trained model, you can use `test.py` and parse t python test.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` -### Deployment +## Performance + +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode -See [here](../../deploy/README.md). +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv5 | N | 8 | 32 | 640x640 | O2 | 377.81s | 520.79 | 491.56 | 27.4% | [yaml](./yolov5n.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-bedf9a93-910v2.ckpt) | +| YOLOv5 | S | 8 | 32 | 640x640 | O2 | 378.18s | 526.49 | 486.30 | 37.6% | [yaml](./yolov5s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-df4a45b6-910v2.ckpt) | + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv5 | N | 8 | 32 | 640x640 | O2 | 233.25s | 650.57 | 393.50 | 27.3% | [yaml](./yolov5n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt) | +| YOLOv5 | S | 8 | 32 | 640x640 | O2 | 166.00s | 650.14 | 393.76 | 37.6% | [yaml](./yolov5s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt) | +| YOLOv5 | M | 8 | 32 | 640x640 | O2 | 256.51s | 712.31 | 359.39 | 44.9% | [yaml](./yolov5m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt) | +| YOLOv5 | L | 8 | 32 | 640x640 | O2 | 274.15s | 723.35 | 353.91 | 48.5% | [yaml](./yolov5l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt) | +| YOLOv5 | X | 8 | 16 | 640x640 | O2 | 436.18s | 569.96 | 224.58 | 50.5% | [yaml](./yolov5x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5x_300e_mAP505-97d36ddc.ckpt) | + + +
+ +### Notes + +- Box mAP: Accuracy reported on the validation set. +- We refer to the official [YOLOV5](https://github.com/ultralytics/yolov5) to reproduce the P5 series model, and the differences are as follows: + 1. We use 8x NPU(Ascend910) for training, and the single-NPU batch size is 32. This is different from the official code. ## References diff --git a/configs/yolov7/README.md b/configs/yolov7/README.md index ba7a91df..c6c851b8 100644 --- a/configs/yolov7/README.md +++ b/configs/yolov7/README.md @@ -9,34 +9,11 @@ YOLOv7 surpasses all known object detectors in both speed and accuracy in the ra -## Results +## Requirements -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | -| YOLOv7 | Tiny | 16 * 8 | 640 | MS COCO 2017 | 37.5 | 6.2M | [yaml](./yolov7-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-d8972c94.ckpt) | -| YOLOv7 | L | 16 * 8 | 640 | MS COCO 2017 | 50.8 | 36.9M | [yaml](./yolov7.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7_300e_mAP508-734ac919.ckpt) | -| YOLOv7 | X | 12 * 8 | 640 | MS COCO 2017 | 52.4 | 71.3M | [yaml](./yolov7-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-x_300e_mAP524-e2f58741.ckpt) | -
- -
-performance tested on Ascend 910*(8p) - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOv7 | Tiny | 16 * 8 | 640 | MS COCO 2017 | 37.5 | 496.21 | 6.2M | [yaml](./yolov7-tiny.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-1d2ddf4b-910v2.ckpt) | -
- -
- -#### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Box mAP: Accuracy reported on the validation set. -- We refer to the official [YOLOV7](https://github.com/WongKinYiu/yolov7) to reproduce the P5 series model, and the differences are as follows: - 1. We use 8x NPU(Ascend910) for training, and the single-NPU batch size for tiny/l/x is 16/16/12. This is different from the official code. +| mindspore | ascend driver | firmware | cann toolkit/kernel +| :-------: | :-----------: | :----------: | :----------------: +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 ## Quick Start @@ -45,6 +22,7 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/ ### Training
+View More #### - Distributed Training @@ -80,9 +58,32 @@ To validate the accuracy of the trained model, you can use `test.py` and parse t python test.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` -### Deployment +## Performance -See [here](../../deploy/README.md). +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode + +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: +| YOLOv7 | Tiny | 8 | 16 | 640x640 | O2 | 363.74s | 352.92 | 362.69 | 37.5% | [yaml](./yolov7-tiny.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-1d2ddf4b-910v2.ckpt) | + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv7 | Tiny | 8 | 16 | 640x640 | O2 | 232.63s | 472.37 | 270.97 | 37.5% | [yaml](./yolov7-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-d8972c94.ckpt) | +| YOLOv7 | L | 8 | 16 | 640x640 | O2 | 290.93s | 678.07 | 188.77 | 50.8% | [yaml](./yolov7.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7_300e_mAP508-734ac919.ckpt) | +| YOLOv7 | X | 8 | 12 | 640x640 | O2 | 404.77s | 636.36 | 150.86 | 52.4% | [yaml](./yolov7-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-x_300e_mAP524-e2f58741.ckpt) | + + +
+ +### Notes + +- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. +- Box mAP: Accuracy reported on the validation set. +- We refer to the official [YOLOV7](https://github.com/WongKinYiu/yolov7) to reproduce the P5 series model, and the differences are as follows: + 1. We use 8x NPU(Ascend910) for training, and the single-NPU batch size for tiny/l/x is 16/16/12. This is different from the official code. ## References diff --git a/configs/yolov8/README.md b/configs/yolov8/README.md index 1ea0386e..e7c052c6 100644 --- a/configs/yolov8/README.md +++ b/configs/yolov8/README.md @@ -7,45 +7,11 @@ Ultralytics YOLOv8, developed by Ultralytics, is a cutting-edge, state-of-the-ar -## Results +## Requirements -### Detection - -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | -| YOLOv8 | N | 16 * 8 | 640 | MS COCO 2017 | 37.2 | 3.2M | [yaml](./yolov8n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-cc07f5bd.ckpt) | -| YOLOv8 | S | 16 * 8 | 640 | MS COCO 2017 | 44.6 | 11.2M | [yaml](./yolov8s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-3086f0c9.ckpt) | -| YOLOv8 | M | 16 * 8 | 640 | MS COCO 2017 | 50.5 | 25.9M | [yaml](./yolov8m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-m_500e_mAP505-8ff7a728.ckpt) | -| YOLOv8 | L | 16 * 8 | 640 | MS COCO 2017 | 52.8 | 43.7M | [yaml](./yolov8l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-l_500e_mAP528-6e96d6bb.ckpt) | -| YOLOv8 | X | 16 * 8 | 640 | MS COCO 2017 | 53.7 | 68.2M | [yaml](./yolov8x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x_500e_mAP537-b958e1c7.ckpt) | -
- -
-performance tested on Ascend 910*(8p) - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOv8 | N | 16 * 8 | 640 | MS COCO 2017 | 37.3 | 373.55 | 3.2M | [yaml](./yolov8n.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-0e737186-910v2.ckpt) | -| YOLOv8 | S | 16 * 8 | 640 | MS COCO 2017 | 44.7 | 365.53 | 11.2M | [yaml](./yolov8s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-fae4983f-910v2.ckpt) | -
- -### Segmentation - -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Mask mAP (%) | Params | Recipe | Download | -|------------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOv8-seg | X | 16 * 8 | 640 | MS COCO 2017 | 52.5 | 42.9 | 71.8M | [yaml](./seg/yolov8x-seg.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt) | -
- -### Notes - -- Box mAP: Accuracy reported on the validation set. -- We refer to the official [YOLOV8](https://github.com/ultralytics/ultralytics) to reproduce the P5 series model. +| mindspore | ascend driver | firmware | cann toolkit/kernel +| :-------: | :-----------: | :----------: | :----------------: +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 ## Quick Start @@ -54,6 +20,7 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/ ### Training
+View More #### - Distributed Training @@ -89,9 +56,47 @@ To validate the accuracy of the trained model, you can use `test.py` and parse t python test.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` -### Deployment +## Performance + +### Detection + +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode + +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv8 | N | 8 | 16 | 640x640 | O2 | 145.89s | 252.79 | 506.35 | 37.3% | [yaml](./yolov8n.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-0e737186-910v2.ckpt) | +| YOLOv8 | S | 8 | 16 | 640x640 | O2 | 172.22s | 251.30 | 509.35 | 44.7% | [yaml](./yolov8s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-fae4983f-910v2.ckpt) | + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv8 | N | 8 | 16 | 640x640 | O2 | 195.63s | 265.13 | 482.78 | 37.2% | [yaml](./yolov8n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-cc07f5bd.ckpt) | +| YOLOv8 | S | 8 | 16 | 640x640 | O2 | 115.60s | 292.68 | 437.34 | 44.6% | [yaml](./yolov8s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-3086f0c9.ckpt) | +| YOLOv8 | M | 8 | 16 | 640x640 | O2 | 185.25s | 383.72 | 333.58 | 50.5% | [yaml](./yolov8m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-m_500e_mAP505-8ff7a728.ckpt) | +| YOLOv8 | L | 8 | 16 | 640x640 | O2 | 175.08s | 429.02 | 298.35 | 52.8% | [yaml](./yolov8l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-l_500e_mAP528-6e96d6bb.ckpt) | +| YOLOv8 | X | 8 | 16 | 640x640 | O2 | 183.68s | 521.97 | 245.22 | 53.7% | [yaml](./yolov8x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x_500e_mAP537-b958e1c7.ckpt) | + + +### Segmentation + +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode + +*coming soon* + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model Name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | Mask mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOv8-seg | X | 8 | 16 | 640x640 | O2 | 183.68s | 641.25 | 199.61 | 52.5% | 42.9% | [yaml](./seg/yolov8x-seg.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt) | + +### Notes + +- Box mAP: Accuracy reported on the validation set. +- We refer to the official [YOLOV8](https://github.com/ultralytics/ultralytics) to reproduce the P5 series model. -See [here](../../deploy/README.md). ## References diff --git a/configs/yolox/README.md b/configs/yolox/README.md index 18d8a8c2..7c643330 100644 --- a/configs/yolox/README.md +++ b/configs/yolox/README.md @@ -6,36 +6,11 @@ YOLOX is a new high-performance detector with some experienced improvements to Y -## Results - -
-performance tested on Ascend 910(8p) with graph mode - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | -| YOLOX | N | 8 * 8 | 416 | MS COCO 2017 | 24.1 | 0.9M | [yaml](./yolox-nano.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-n_300e_map241-ec9815e3.ckpt) | -| YOLOX | Tiny | 8 * 8 | 416 | MS COCO 2017 | 33.3 | 5.1M | [yaml](./yolox-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-tiny_300e_map333-e5ae3a2e.ckpt) | -| YOLOX | S | 8 * 8 | 640 | MS COCO 2017 | 40.7 | 9.0M | [yaml](./yolox-s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-s_300e_map407-0983e07f.ckpt) | -| YOLOX | M | 8 * 8 | 640 | MS COCO 2017 | 46.7 | 25.3M | [yaml](./yolox-m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-m_300e_map467-1db321ee.ckpt) | -| YOLOX | L | 8 * 8 | 640 | MS COCO 2017 | 49.2 | 54.2M | [yaml](./yolox-l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-l_300e_map492-52a4ab80.ckpt) | -| YOLOX | X | 8 * 8 | 640 | MS COCO 2017 | 51.6 | 99.1M | [yaml](./yolox-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-x_300e_map516-52216d90.ckpt) | -| YOLOX | Darknet53 | 8 * 8 | 640 | MS COCO 2017 | 47.7 | 63.7M | [yaml](./yolox-darknet53.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-darknet53_300e_map477-b5fcaba9.ckpt) | -
- -
-performance tested on Ascend 910*(8p) - -| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download | -|--------| :---: | :---: | :---: |--------------| :---: | :---: | :---: | :---: | :---: | -| YOLOX | S | 8 * 8 | 640 | MS COCO 2017 | 41.0 | 242.15 | 9.0M | [yaml](./yolox-s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolox/yolox-s_300e_map407-cebd0183-910v2.ckpt) | -
- -
+## Requirements -#### Notes - -- Box mAP: Accuracy reported on the validation set. -- We refer to the official [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) to reproduce the results. +| mindspore | ascend driver | firmware | cann toolkit/kernel +| :-------: | :-----------: | :----------: | :----------------: +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 ## Quick Start @@ -44,6 +19,7 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/ ### Training
+View More #### - Distributed Training @@ -79,9 +55,34 @@ To validate the accuracy of the trained model, you can use `test.py` and parse t python test.py --config ./configs/yolox/yolox-s.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` -### Deployment +## Performance + +Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode -See [here](../../deploy/README.md). +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOX | S | 8 | 8 | 640x640 | O2 | 299.01s | 177.65 | 360.26 | 41.0% | [yaml](./yolox-s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolox/yolox-s_300e_map407-cebd0183-910v2.ckpt) | + + +Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode + +| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe |weight | +| :--------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| YOLOX | N | 8 | 8 | 416x416 | O2 | 202.49s | 138.84 | 460.96 | 24.1% | [yaml](./yolox-nano.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-n_300e_map241-ec9815e3.ckpt) | +| YOLOX | Tiny | 8 | 8 | 416x416 | O2 | 169.71s | 126.85 | 504.53 | 33.3% | [yaml](./yolox-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-tiny_300e_map333-e5ae3a2e.ckpt) | +| YOLOX | S | 8 | 8 | 640x640 | O2 | 202.46s | 243.99 | 262.31 | 40.7% | [yaml](./yolox-s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-s_300e_map407-0983e07f.ckpt) | +| YOLOX | M | 8 | 8 | 640x640 | O2 | 212.78s | 267.68 | 239.09 | 46.7% | [yaml](./yolox-m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-m_300e_map467-1db321ee.ckpt) | +| YOLOX | L | 8 | 8 | 640x640 | O2 | 262.52s | 316.78 | 202.03 | 49.2% | [yaml](./yolox-l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-l_300e_map492-52a4ab80.ckpt) | +| YOLOX | X | 8 | 8 | 640x640 | O2 | 341.33s | 415.67 | 153.97 | 51.6% | [yaml](./yolox-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-x_300e_map516-52216d90.ckpt) | +| YOLOX |Darknet53| 8 | 8 | 640x640 | O2 | 198.15s | 407.53 | 157.04 | 47.7% | [yaml](./yolox-darknet53.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-darknet53_300e_map477-b5fcaba9.ckpt) | + + +
+ +### Notes + +- Box mAP: Accuracy reported on the validation set. +- We refer to the official [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) to reproduce the results. ## References