diff --git a/modelcenter/PP-PicoDet/introduction_cn.ipynb b/modelcenter/PP-PicoDet/introduction_cn.ipynb index d53e67823c95b2b4e0a31e25274505bfb981a5b2..cba5701734eaa8029db4bba52dda543bac64b70c 100644 --- a/modelcenter/PP-PicoDet/introduction_cn.ipynb +++ b/modelcenter/PP-PicoDet/introduction_cn.ipynb @@ -24,7 +24,7 @@ "## 2. 模型效果\n", "PP-Picodet与其他轻量级模型的精度速度对比图如下所示:\n", "
\n", - " \n", + " \n", "
" ] }, @@ -37,12 +37,19 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { diff --git a/modelcenter/PP-PicoDet/introduction_en.ipynb b/modelcenter/PP-PicoDet/introduction_en.ipynb index f7d859d8764d90209255d2e5122a8cb2ba5c6c00..d8f0e42e46721833ce3fe038fbc3b68dc1b56da8 100644 --- a/modelcenter/PP-PicoDet/introduction_en.ipynb +++ b/modelcenter/PP-PicoDet/introduction_en.ipynb @@ -23,7 +23,7 @@ "## 2. Model Effects\n", "The accuracy and speed comparison of PP-Picodet and other lightweight models is shown below:\n", "
\n", - " \n", + " \n", "
" ] }, @@ -47,7 +47,8 @@ "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { diff --git a/modelcenter/PP-YOLO/introduction_cn.ipynb b/modelcenter/PP-YOLO/introduction_cn.ipynb index fd886fd51a62835202fc72166694480f2192223f..523eb2fa19f2ef39f385ff99d285de40c20c8957 100644 --- a/modelcenter/PP-YOLO/introduction_cn.ipynb +++ b/modelcenter/PP-YOLO/introduction_cn.ipynb @@ -16,7 +16,7 @@ "PP-YOLO在[COCO](http://cocodataset.org) test-dev2017数据集上精度达到45.9%,在单卡V100上FP32推理速度为72.9 FPS, V100上开启TensorRT下FP16推理速度为155.6 FPS。\n", "\n", "
\n", - " \n", + " \n", "
" ] }, @@ -40,7 +40,8 @@ "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { @@ -61,7 +62,7 @@ }, "outputs": [], "source": [ - "!python -m paddle.distributed.launch --log_dir=./ppyolo_dygraph/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml &>ppyolo_dygraph.log 2>&1 &" + "!python -m paddle.distributed.launch --log_dir=./ppyolo_dygraph/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml" ] }, { diff --git a/modelcenter/PP-YOLO/introduction_en.ipynb b/modelcenter/PP-YOLO/introduction_en.ipynb index 04f0415fe7b1a51da12f68751741bde84b3ac260..d7a30b1ed1cba0923a8dc6b75b54175fe59ee3c4 100644 --- a/modelcenter/PP-YOLO/introduction_en.ipynb +++ b/modelcenter/PP-YOLO/introduction_en.ipynb @@ -16,7 +16,7 @@ "PP-YOLO reached mmAP(IoU=0.5:0.95) as 45.9% on COCO test-dev2017 dataset, and inference speed of FP32 on single V100 is 72.9 FPS, inference speed of FP16 with TensorRT on single V100 is 155.6 FPS.\n", "\n", "
\n", - " \n", + " \n", "
" ] }, @@ -40,7 +40,8 @@ "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { @@ -61,7 +62,7 @@ }, "outputs": [], "source": [ - "!python -m paddle.distributed.launch --log_dir=./ppyolo_dygraph/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml &>ppyolo_dygraph.log 2>&1 &" + "!python -m paddle.distributed.launch --log_dir=./ppyolo_dygraph/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml" ] }, { diff --git a/modelcenter/PP-YOLOE+/introduction_cn.ipynb b/modelcenter/PP-YOLOE+/introduction_cn.ipynb index 423c076481e8e34c81f415d6e04ccbe63fef9db7..98e8058939a4f806dca169020f15a77399f3408a 100644 --- a/modelcenter/PP-YOLOE+/introduction_cn.ipynb +++ b/modelcenter/PP-YOLOE+/introduction_cn.ipynb @@ -15,7 +15,7 @@ "## 2. 模型效果\n", "PP-YOLOE+_l在COCO test-dev2017达到了53.3的mAP, 同时其速度在Tesla V100上达到了78.1 FPS。如下图所示,PP-YOLOE+_s/m/x同样具有卓越的精度速度性价比。\n", "
\n", - " \n", + " \n", "
" ] }, @@ -28,12 +28,19 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { @@ -114,10 +121,10 @@ "outputs": [], "source": [ "# 推理单张图片\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16\n", + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16\n", "\n", "# 推理文件夹下的所有图片\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=trt_fp16" + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=trt_fp16" ] }, { @@ -168,10 +175,10 @@ "outputs": [], "source": [ "# 推理单张图片\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=paddle\n", + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=paddle\n", "\n", "# 推理文件夹下的所有图片\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=paddle" + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=paddle" ] }, { diff --git a/modelcenter/PP-YOLOE+/introduction_en.ipynb b/modelcenter/PP-YOLOE+/introduction_en.ipynb index 775d0fb01c314c4ea1dfe1c6d56b8d6970eada88..3feaa15b609c70beaceb2173e17d4b96403584e1 100644 --- a/modelcenter/PP-YOLOE+/introduction_en.ipynb +++ b/modelcenter/PP-YOLOE+/introduction_en.ipynb @@ -15,7 +15,7 @@ "## 2. Model Effects\n", "PP-YOLOE+_l achieves 53.3 mAP on COCO test-dev2017 dataset with 78.1 FPS on Tesla V100. While using TensorRT FP16, PP-YOLOE+_l can be further accelerated to 149.2 FPS. PP-YOLOE+_s/m/x also have excellent accuracy and speed performance as shown below.\n", "
\n", - " \n", + " \n", "
" ] }, @@ -39,7 +39,8 @@ "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { @@ -120,10 +121,10 @@ "outputs": [], "source": [ "# inference single image\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16\n", + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16\n", "\n", "# inference all images in the directory\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=trt_fp16" + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=trt_fp16" ] }, { @@ -180,10 +181,10 @@ "outputs": [], "source": [ "# inference single image\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=paddle\n", + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=paddle\n", "\n", "# inference all images in the directory\n", - "CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=paddle" + "!CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=paddle" ] }, { diff --git a/modelcenter/PP-YOLOE/introduction_cn.ipynb b/modelcenter/PP-YOLOE/introduction_cn.ipynb index 8088907fa14eca3d6516943172a6cdecf24b48ae..3bd6a5ed5c447e07b391c0f80d837eb7394625a7 100644 --- a/modelcenter/PP-YOLOE/introduction_cn.ipynb +++ b/modelcenter/PP-YOLOE/introduction_cn.ipynb @@ -39,7 +39,8 @@ "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { diff --git a/modelcenter/PP-YOLOE/introduction_en.ipynb b/modelcenter/PP-YOLOE/introduction_en.ipynb index ab8e09cb528f81cb4268e138a5ce0e38decf6ca4..add9b2c9e518e56f279b99144f82cc4e507d0d96 100644 --- a/modelcenter/PP-YOLOE/introduction_en.ipynb +++ b/modelcenter/PP-YOLOE/introduction_en.ipynb @@ -40,7 +40,8 @@ "source": [ "%cd ~/work\n", "!git clone https://gitee.com/paddlepaddle/PaddleDetection\n", - "%cd PaddleDetection" + "%cd PaddleDetection\n", + "!pip install -r requirements.txt" ] }, { diff --git a/modelcenter/PP-YOLOv2/introduction_cn.ipynb b/modelcenter/PP-YOLOv2/introduction_cn.ipynb index 6b381b6b95071f71836aeb7a6d2c1a1c51b9e554..fd43dcd78727cfe29354ad452987446b3fc45cc6 100644 --- a/modelcenter/PP-YOLOv2/introduction_cn.ipynb +++ b/modelcenter/PP-YOLOv2/introduction_cn.ipynb @@ -153,8 +153,8 @@ "outputs": [], "source": [ "# 在GPU上预测一张图片\n", - "export CUDA_VISIBLE_DEVICES=0\n", - "python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg" + "!export CUDA_VISIBLE_DEVICES=0\n", + "!python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg" ] }, { @@ -165,7 +165,7 @@ "\n", "结果如下图:\n", "
\n", - "\n", + "\n", "
\n", "\n", "\n" diff --git a/modelcenter/PP-YOLOv2/introduction_en.ipynb b/modelcenter/PP-YOLOv2/introduction_en.ipynb index 260196af367088f80ac46ed62d254dbc181c0d5f..fc094724529cb13ef8eec0b2e4e88d985f4f835d 100644 --- a/modelcenter/PP-YOLOv2/introduction_en.ipynb +++ b/modelcenter/PP-YOLOv2/introduction_en.ipynb @@ -145,8 +145,8 @@ "outputs": [], "source": [ "# Predict a picture on the GPU.\n", - "export CUDA_VISIBLE_DEVICES=0\n", - "python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg" + "!export CUDA_VISIBLE_DEVICES=0\n", + "!python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg" ] }, { @@ -156,8 +156,9 @@ "An image with the predicted result is generated under the output folder.\n", "\n", "The result is as follows:\n", - "\n", - "![](https://ai-studio-static-online.cdn.bcebos.com/76fb0d0b60fe4fe39cc7302f4c25818133f970ebdf924d2d85f70f25a586aab9)\n" + "
\n", + "\n", + "
\n" ] }, {