update readme for mindspore version of 2.3.1

mindspore-lab · Nov 9, 2024 · 7c1ceb2 · 7c1ceb2
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diff --git 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.
 
   <details onclose>
-
+  <summary><b>View More</b></summary>
   ```
     coco/
       {train,val}2017.txt

diff --git a/README.md b/README.md
@@ -17,7 +17,7 @@ The following is the corresponding `mindyolo` versions and supported `mindspore`
 |  mindyolo  |  mindspore  |
 |    :--:    |     :--:    |
 |   master   |    master   |
-|    0.4     |    2.3.0    |
+|    0.4     | 2.3.1/2.3.0 |
 |    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
@@ -9,22 +9,22 @@ We present some updates to YOLO! We made a bunch of little design changes to mak
 <img src="https://raw.githubusercontent.com/zhanghuiyao/pics/main/mindyolo202304071143644.png"/>
 </div>
 
-## Results
+## Performance
 
 <details open markdown>
-<summary><b>performance tested on Ascend 910(8p) with graph mode</b></summary>
+<summary><b>Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode</b></summary>
 
-| 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)         |
+| model name | cards  | batch size | resolution |  jit level  | graph compile  |     mAP     | ms/step | img/s  |        recipe           | weight                                                              |
+|  :------:  |  :---: |   :---:    |    :---:   |    :---:    |     :---:      |    :---:    |  :---:  | :---:  |         :---:           |        :---:       |
+|   YOLOv3   |    8   |    16      |   640x640  |     O2      |    160.80s     |    45.5%    | 409.66  | 312.45 |  [yaml](./yolov3.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-adfb27af.ckpt)            |
 </details>
 
 <details open markdown>
-<summary><b>performance tested on Ascend 910*(8p)</b></summary>
+<summary><b>Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode</b></summary>
 
-| 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)         |
+| model name | cards  | batch size | resolution |  jit level  | graph compile  |      mAP    | ms/step | img/s  |        recipe           | weight                                                                                        |
+|  :------:  |  :---: |   :---:    |   :---:    |    :---:    |     :---:      |    :---:    |  :---:  | :---:  |      :---:           |        :---:       |
+|   YOLOv3   |    8   |    16      |   640x640  |     O2      |    274.32s     |    46.6%    | 383.68  | 333.61 |  [yaml](./yolov3.yaml)  | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-81895f09-910v2.ckpt)         |
 </details>
 
 <br>
@@ -38,9 +38,16 @@ We present some updates to YOLO! We made a bunch of little design changes to mak
 
 Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/blob/master/GETTING_STARTED.md) in MindYOLO for details.
 
+### Requirements
+
+| mindspore | ascend driver | firmware     | cann toolkit/kernel
+| :-------: | :-----------: | :----------: | :----------------:
+| 2.3.1     | 24.1.RC2      | 7.3.0.1.231  | 8.0.RC2.beta1
+
 ### Training
 
-<details open>
+<details open markdown>
+<summary><b>View More</b></summary>
 
 #### - Pretraining Model
 

diff --git a/configs/yolov4/README.md b/configs/yolov4/README.md
@@ -23,23 +23,23 @@ AP (65.7% AP50) for the MS COCO dataset at a realtime speed of 65 FPS on Tesla V
 <img src="https://github.com/yuedongli1/images/raw/master/mindyolo20230509.png"/>
 </div>
 
-## Results
+## Performance
 
 <details open markdown>
-<summary><b>performance tested on Ascend 910(8p) with graph mode</b></summary>
+<summary><b>Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode</b></summary>
 
-| 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) |
+| model name |     backbone       | cards  | batch size | resolution |  jit level  | graph compile |     mAP     | ms/step | img/s  |         recipe              | weight                                                                           
+| :--------: |        :---:       |  :---: |   :---:    |   :---:    |    :---:    |     :---:     |    :---:    |  :---:  | :---:  |         :---:               |        :---:       |
+|   YOLOv4   |    CSPDarknet53    |    8   |     16     |   608x608  |     O2      |    188.52s    |    45.4%    | 505.98  | 252.97 | [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    |    45.8%    | 443.21  | 288.80 | [yaml](./yolov4-silu.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_silu_320e_map458-bdfc3205.ckpt) |
 </details>
 
 <details open markdown>
-<summary><b>performance tested on Ascend 910*(8p)</b></summary>
+<summary><b>Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode</b></summary>
 
-| 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)      |
+| model name |     backbone    | cards  | batch size | resolution |  jit level  | graph compile |     mAP     | ms/step | img/s  |          recipe        | weight                                                                                                             |
+| :--------: |      :---:      |  :---: |   :---:    |   :---:    |    :---:    |     :---:     |    :---:    |  :---:  | :---:  |          :---:         |        :---:       |
+|   YOLOv4   |   CSPDarknet53  |    8   |     16     |   608x608  |     O2      |    467.47s    |    46.1%    | 308.43  | 415.01 | [yaml](./yolov4.yaml)  | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-64b8506f-910v2.ckpt)      |
 </details>
 
 <br>
@@ -52,9 +52,16 @@ AP (65.7% AP50) for the MS COCO dataset at a realtime speed of 65 FPS on Tesla V
 
 Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/blob/master/GETTING_STARTED.md) in MindYOLO for details.
 
+### Requirements
+
+| mindspore | ascend driver | firmware     | cann toolkit/kernel
+| :-------: | :-----------: | :----------: | :----------------:
+| 2.3.1     | 24.1.RC2      | 7.3.0.1.231  | 8.0.RC2.beta1
+
 ### Training
 
 <details open>
+<summary><b>View More</b></summary>
 
 #### - Pretraining Model
 

diff --git a/configs/yolov5/README.md b/configs/yolov5/README.md
@@ -6,27 +6,27 @@ YOLOv5 is a family of object detection architectures and models pretrained on th
 <img src="https://raw.githubusercontent.com/zhanghuiyao/pics/main/mindyolo20230407113509.png"/>
 </div>
 
-## Results
+## Performance
 
 <details open markdown>
-<summary><b>performance tested on Ascend 910(8p) with graph mode</b></summary>
-
-| 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)                  |
+<summary><b>Experiments are tested on Ascend 910(8p) with mindspore 2.3.1 graph mode</b></summary>
+
+|  model name  |  scale  | cards  | batch size | resolution |  jit level  | graph compile |     mAP     | ms/step | img/s |           recipe           | weight                                                                       |
+|  :--------:  |  :---:  |  :---: |    :---:   |   :---:    |    :---:    |     :---:     |    :---:    |  :---:  | :---:｜           :---:            |        :---:       |
+|   YOLOv5     |    N    |    8   |    32      |  640x640   |     O2      |    233.25s    |    27.3%    | 650.57  | 393.50 ｜   [yaml](./yolov5n.yaml)   | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt)                  |
+|   YOLOv5     |    S    |    8   |    32      |  640x640   |     O2      |    166.00s    |    37.6%    | 650.14  | 393.76 ｜   [yaml](./yolov5s.yaml)   | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt)                  |
+|   YOLOv5     |    M    |    8   |    32      |  640x640   |     O2      |    256.51s    |    44.9%    | 712.31  | 359.39 ｜   [yaml](./yolov5m.yaml)   | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt)                  |
+|   YOLOv5     |    L    |    8   |    32      |  640x640   |     O2      |    274.15s    |    48.5%    | 723.35  | 353.91 ｜   [yaml](./yolov5l.yaml)   | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt)                  |
+|   YOLOv5     |    X    |    8   |    16      |  640x640   |     O2      |    436.18s    |    50.5%    | 569.96  | 224.58 ｜   [yaml](./yolov5x.yaml)   | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5x_300e_mAP505-97d36ddc.ckpt)                  |
 </details>
 
 <details open markdown>
-<summary><b>performance tested on Ascend 910*(8p)</b></summary>
+<summary><b>Experiments are tested on Ascend 910*(8p) with mindspore 2.3.1 graph mode</b></summary>
 
-| 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)                  |
+| model name   |  scale  | cards  | batch size | resolution |  jit level  | graph compile |     mAP     | ms/step | img/s |           recipe           | weight                                                                                                |
+|  :--------:  |  :---:  |  :---: |   :---:    |   :---:    |    :---:    |     :---:     |    :---:    |  :---:  | :---:｜           :---:            |        :---:       |
+|   YOLOv5     |    N    |    8   |    32      |  640x640   |     O2      |    377.81s    |     27.4%   | 520.79  | 491.56 ｜   [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    |     37.6%   | 526.49  | 486.30 ｜   [yaml](./yolov5s.yaml)   | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-df4a45b6-910v2.ckpt)       |
 </details>
 
 <br>
@@ -41,9 +41,16 @@ YOLOv5 is a family of object detection architectures and models pretrained on th
 
 Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/blob/master/GETTING_STARTED.md) in MindYOLO for details.
 
+### Requirements
+
+| mindspore | ascend driver | firmware     | cann toolkit/kernel
+| :-------: | :-----------: | :----------: | :----------------:
+| 2.3.1     | 24.1.RC2      | 7.3.0.1.231  | 8.0.RC2.beta1
+
 ### Training
 
 <details open>
+<summary><b>View More</b></summary>
 
 #### - Distributed Training