remove gpu's description in md

GETTING_STARTED.md

@@ -13,9 +13,6 @@ This document provides a brief introduction to the usage of built-in command-lin
 ```
 # Run with Ascend (By default)
 python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg
-
-# Run with GPU
-python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg --device_target=GPU
 ```
 
 
@@ -48,23 +45,23 @@ to understand their behavior. Some common arguments are:
   ```
   </details>
 
-* To train a model on 1 NPU/GPU/CPU:
+* To train a model on 1 NPU/CPU:
   ```
   python train.py --config ./configs/yolov7/yolov7.yaml 
   ```
-* To train a model on 8 NPUs/GPUs:
+* To train a model on 8 NPUs:
   ```
   msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log python train.py --config ./configs/yolov7/yolov7.yaml  --is_parallel True
   ```
-* To evaluate a model's performance on 1 NPU/GPU/CPU:
+* To evaluate a model's performance on 1 NPU/CPU:
   ```
   python test.py --config ./configs/yolov7/yolov7.yaml --weight /path_to_ckpt/WEIGHT.ckpt
   ```
-* To evaluate a model's performance 8 NPUs/GPUs:
+* To evaluate a model's performance 8 NPUs:
   ```
   msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log python test.py --config ./configs/yolov7/yolov7.yaml --weight /path_to_ckpt/WEIGHT.ckpt --is_parallel True
   ```
-*Notes: (1) The default hyper-parameter is used for 8-card training, and some parameters need to be adjusted in the case of a single card. (2) The default device is Ascend, and you can modify it by specifying 'device_target' as Ascend/GPU/CPU, as these are currently supported.*
+*Notes: (1) The default hyper-parameter is used for 8-card training, and some parameters need to be adjusted in the case of a single card. (2) The default device is Ascend, and you can modify it by specifying 'device_target' as Ascend/CPU, as these are currently supported.*
 * For more options, see `train/test.py -h`.
 
 * Notice that if you are using `msrun` startup with 2 devices, please add `--bind_core=True` to improve performance. For example:

GETTING_STARTED_CN.md

@@ -11,9 +11,6 @@
 ```shell
 # NPU (默认)
 python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg
-
-# GPU
-python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg --device_target=GPU
 ```
 
 有关命令行参数的详细信息，请参阅`demo/predict.py -h`，或查看其[源代码](https://github.com/mindspore-lab/mindyolo/blob/master/deploy/predict.py)。
@@ -45,27 +42,27 @@ python demo/predict.py --config ./configs/yolov7/yolov7.yaml --weight=/path_to_c
   ```
   </details>
 
-* 在单卡NPU/GPU/CPU上训练模型：
+* 在单卡NPU/CPU上训练模型：
 
   ```shell
   python train.py --config ./configs/yolov7/yolov7.yaml 
   ```
-* 在多卡NPU/GPU上进行分布式模型训练，以8卡为例:
+* 在多卡NPU上进行分布式模型训练，以8卡为例:
   ```shell
   msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log python train.py --config ./configs/yolov7/yolov7.yaml  --is_parallel True
   ```
-* 在单卡NPU/GPU/CPU上评估模型的精度：
+* 在单卡NPU/CPU上评估模型的精度：
 
   ```shell
   python test.py --config ./configs/yolov7/yolov7.yaml --weight /path_to_ckpt/WEIGHT.ckpt
   ```
-* 在多卡NPU/GPU上进行分布式评估模型的精度：
+* 在多卡NPU上进行分布式评估模型的精度：
 
   ```shell
   msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log python test.py --config ./configs/yolov7/yolov7.yaml --weight /path_to_ckpt/WEIGHT.ckpt --is_parallel True
   ```
 
-*注意：默认超参为8卡训练，单卡情况需调整部分参数。 默认设备为Ascend，您可以指定'device_target'的值为Ascend/GPU/CPU。*
+*注意：默认超参为8卡训练，单卡情况需调整部分参数。 默认设备为Ascend，您可以指定'device_target'的值为Ascend/CPU。*
 * 有关更多选项，请参阅 `train/test.py -h`.
 * 在云脑上进行训练，请在[这里](./tutorials/cloud/modelarts_CN.md)查看。
 

configs/yolov10/README.md

@@ -48,11 +48,10 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov10_log python train.py --config ./configs/yolov10/yolov10n.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -64,7 +63,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov19/yolov10n.yaml --device_target Ascend
 ```
 

configs/yolov3/README.md

@@ -37,11 +37,10 @@ python mindyolo/utils/convert_weight_darknet53.py
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov3_log python train.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html)
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -53,7 +52,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend
 ```
 

configs/yolov4/README.md

@@ -51,11 +51,10 @@ python mindyolo/utils/convert_weight_cspdarknet53.py
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov4_log  python train.py --config ./configs/yolov4/yolov4-silu.yaml --device_target Ascend --is_parallel True --epochs 320
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html)
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -72,7 +71,7 @@ multiprocessing/semaphore_tracker.py: 144 UserWarning: semaphore_tracker: There
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov4/yolov4-silu.yaml --device_target Ascend --epochs 320
 ```
 

configs/yolov5/README.md

@@ -25,11 +25,10 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov5_log  python train.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html)
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -41,7 +40,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend
 ```
 

configs/yolov7/README.md

@@ -28,11 +28,10 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7_log python train.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html)
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -44,7 +43,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend
 ```
 

configs/yolov8/README.md

@@ -26,11 +26,10 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov8_log python train.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html)
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -42,7 +41,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend
 ```
 

configs/yolov9/README.md

@@ -56,11 +56,10 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov9_log python train.py --config ./configs/yolov9/yolov9-t.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -72,7 +71,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python train.py --config ./configs/yolov9/yolov9-t.yaml --device_target Ascend
 ```
 

configs/yolox/README.md

@@ -25,11 +25,10 @@ Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/
 
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolox_log python train.py --config ./configs/yolox/yolox-s.yaml --device_target Ascend --is_parallel True
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above msrun command.
 **Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html)
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py).
@@ -41,7 +40,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please firstly run:
 
 ```shell
-# standalone 1st stage training on a CPU/GPU/Ascend device
+# standalone 1st stage training on a CPU/Ascend device
 python train.py --config ./configs/yolox/yolox-s.yaml --device_target Ascend
 ```
 

examples/finetune_SHWD/README.md

@@ -111,13 +111,13 @@ optimizer:
 * anchor可根据实际物体大小进行调整
 
 由于SHWD训练集只有约6000张图片，选用yolov7-tiny模型进行训练。
-* 在多卡NPU/GPU上进行分布式模型训练，以8卡为例:
+* 在多卡NPU上进行分布式模型训练，以8卡为例:
 
   ```shell
   msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov7-tiny_log python train.py --config ./examples/finetune_SHWD/yolov7-tiny_shwd.yaml --is_parallel True
   ```
 
-* 在单卡NPU/GPU/CPU上训练模型：
+* 在单卡NPU/CPU上训练模型：
 
   ```shell
   python train.py --config ./examples/finetune_SHWD/yolov7-tiny_shwd.yaml 

tutorials/configuration_CN.md

@@ -24,7 +24,7 @@ __BASE__: [
 ## 基础参数
 
 ### 参数说明
-   - device_target: 所用设备，Ascend/GPU/CPU
+   - device_target: 所用设备，Ascend/CPU
    - save_dir: 运行结果保存路径，默认为./runs
    - log_interval: 打印日志step间隔，默认为100
    - is_parallel: 是否分布式训练，默认为False