Technology Sharing

The domestic framework PaddleClas combines with Swanlab for weed classification

2024-07-12

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1. Project Introduction

Weeds are one of the main problems in agriculture, posing a serious threat to crop growth and yield. Traditional manual identification and management methods are inefficient and imprecise, so advanced computer vision technology is needed to improve the efficiency and quality of agricultural production. As a deep learning model, ResNet performs well in processing complex image classification tasks. It can not only effectively solve the problems of diverse and complex weeds in farmland, but also promote the development of intelligent agriculture, reduce dependence on chemical pesticides, and achieve the goal of sustainable agricultural development. By using ResNet for weed classification, farmers can be provided with smarter and more accurate agricultural management solutions, which can promote the improvement of agricultural production efficiency and the modernization of the agricultural industry. Therefore, this project uses the domestic framework PaddleClas+Swanlab+Gradio+Swanhub for weed classification experiments.

PaddlePaddle is an enterprise-level deep learning platform developed by Baidu, which is designed to support the full process of deep learning applications from model development to deployment. It provides a wealth of tools and libraries to support a variety of deep learning tasks, including image processing, natural language processing, speech recognition, etc.PaddlePaddle

PaddleClas is a tool library in the Paddle framework specifically for image classification tasks. It provides a complete set of end-to-end solutions, including data processing, model definition, training, evaluation, and deployment, aiming to help developers quickly build and deploy efficient image classification models.PaddleClas

SwanLab is an open source, lightweight AI experiment tracking tool that improves the ML experiment tracking and collaboration experience by providing a friendly API combined with hyperparameter tracking, indicator recording, online collaboration and other functions.Welcome to SwanLab | SwanLab Official Documentation

Swanhub is an open source model collaboration and sharing community developed by Geek Studio. It provides AI developers with functions such as AI model hosting, training records, model result display, and API rapid deployment.Welcome to Swanhub

Gradio is an open source Python library designed to help data scientists, researchers, and developers working in the field of machine learning quickly create and share user interfaces for machine learning models.Gradio

2. Preparation

2.1 Environment Installation

Install the following 3 libraries:

  1. paddle
  2. swanlab
  3. gradio

Installation command:

pip install paddle swanlab gradio

2.2 Download the dataset

Weed Classification Dataset: DeepWeeds

  1. DeepWeeds
  2. --images
  3. ----1.jpg
  4. ----2.jpg
  5. --train.txt
  6. --val.txt
  7. --test.txt
  8. --classnames.txt

Their respective functions and significance:

1. DeepWeeds folder: This folder is used to store the image folder images, training set test set validation set files and label files

2. images folder: This folder is used to save training, testing, and validation image folders.

3. train.txt, val.txt, test.txt files: These files are used to save the training, test, and validation set image paths and categories.

4. classnames file: used to save category labels

2.3 Download PaddleClas framework

Model link:PaddleClas Model

After decompression, you will get the PaddleClas folder.

2.4 Create a file directory

Create app.py in the PaddleClas folder.

Its function is to run the Gradio Demo script

3. ResNet model training

3.1 Modify configuration

First, find ppcls-->configs-->ImageNet-->Res2Net-->Res2Net50_14w_8s.yaml in the PaddleClas folder.

Modify epochs to 100, class class_num to 9, training image path, verification image path and label file. A total of 7 changes were made.

  1. # global configs
  2. Global:
  3.   checkpoints: null
  4.   pretrained_model: null
  5.   output_dir: ./output/
  6.   device: gpu
  7.   save_interval: 1
  8.   eval_during_train: True
  9.   eval_interval: 1
  10.   epochs: 100###########################1##############################
  11.   print_batch_step: 10
  12.   use_visualdl: False
  13.   # used for static mode and model export
  14.   image_shape: [3, 224, 224]
  15.   save_inference_dir: ./inference
  16.  
  17. # model architecture
  18. Arch:
  19.   name: Res2Net50_14w_8s
  20.   class_num: 9############################2##############################
  21.  
  22. # loss function config for traing/eval process
  23. Loss:
  24.   Train:
  25.     - CELoss:
  26.         weight: 1.0
  27.         epsilon: 0.1
  28.   Eval:
  29.     - CELoss:
  30.         weight: 1.0
  31.  
  32.  
  33. Optimizer:
  34.   name: Momentum
  35.   momentum: 0.9
  36.   lr:
  37.     name: Cosine
  38.     learning_rate: 0.1
  39.   regularizer:
  40.     name: 'L2'
  41.     coeff: 0.0001
  42.  
  43.  
  44. # data loader for train and eval
  45. DataLoader:
  46.   Train:
  47.     dataset:
  48.       name: ImageNetDataset
  49.       image_root: ./weeds/images/#################3#######################
  50.       cls_label_path: ./weeds/train.txt###########4########################
  51.       transform_ops:
  52.         - DecodeImage:
  53.             to_rgb: True
  54.             channel_first: False
  55.         - RandCropImage:
  56.             size: 224
  57.         - RandFlipImage:
  58.             flip_code: 1
  59.         - NormalizeImage:
  60.             scale: 1.0/255.0
  61.             mean: [0.485, 0.456, 0.406]
  62.             std: [0.229, 0.224, 0.225]
  63.             order: ''
  64.       batch_transform_ops:
  65.         - MixupOperator:
  66.             alpha: 0.2
  67.  
  68.     sampler:
  69.       name: DistributedBatchSampler
  70.       batch_size: 64
  71.       drop_last: False
  72.       shuffle: True
  73.     loader:
  74.       num_workers: 4
  75.       use_shared_memory: True
  76.  
  77.   Eval:
  78.     dataset: 
  79.       name: ImageNetDataset
  80.       image_root: ./DeepWeeds/images/###############5#######################
  81.       cls_label_path: ./DeepWeeds/val.txt###########6########################
  82.       transform_ops:
  83.         - DecodeImage:
  84.             to_rgb: True
  85.             channel_first: False
  86.         - ResizeImage:
  87.             resize_short: 256
  88.         - CropImage:
  89.             size: 224
  90.         - NormalizeImage:
  91.             scale: 1.0/255.0
  92.             mean: [0.485, 0.456, 0.406]
  93.             std: [0.229, 0.224, 0.225]
  94.             order: ''
  95.     sampler:
  96.       name: DistributedBatchSampler
  97.       batch_size: 64
  98.       drop_last: False
  99.       shuffle: False
  100.     loader:
  101.       num_workers: 4
  102.       use_shared_memory: True
  103.  
  104. Infer:
  105.   infer_imgs: docs/images/inference_deployment/whl_demo.jpg
  106.   batch_size: 10
  107.   transforms:
  108.     - DecodeImage:
  109.         to_rgb: True
  110.         channel_first: False
  111.     - ResizeImage:
  112.         resize_short: 256
  113.     - CropImage:
  114.         size: 224
  115.     - NormalizeImage:
  116.         scale: 1.0/255.0
  117.         mean: [0.485, 0.456, 0.406]
  118.         std: [0.229, 0.224, 0.225]
  119.         order: ''
  120.     - ToCHWImage:
  121.   PostProcess:
  122.     name: Topk
  123.     topk: 5
  124.     class_id_map_file: ./DeepWeeds/classnaems.txt###########7##################
  125.  
  126. Metric:
  127.   Train:
  128.   Eval:
  129.     - TopkAcc:
  130.         topk: [1, 5]

3.2 Using Swanlab

Find tools-->train.py in the PaddleClas folder. Initialize swanlab

  1. # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  2.  #
  3.  # Licensed under the Apache License, Version 2.0 (the "License");
  4.  # you may not use this file except in compliance with the License.
  5.  # You may obtain a copy of the License at
  6.  #
  7.  #     http://www.apache.org/licenses/LICENSE-2.0
  8.  #
  9.  # Unless required by applicable law or agreed to in writing, software
  10.  # distributed under the License is distributed on an "AS IS" BASIS,
  11.  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12.  # See the License for the specific language governing permissions and
  13.  # limitations under the License.
  14.  
  15. from __future__ import absolute_import
  16. from __future__ import division
  17. from __future__ import print_function
  18. import os
  19. import sys
  20.  
  21.  __dir__ = os.path.dirname(os.path.abspath(__file__))
  22.  sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
  23.  
  24. from ppcls.utils import config
  25. from ppcls.engine.engine import Engine
  26. import swanlab
  27. # -*- coding: utf-8 -*-
  28.  
  29. if __name__ == "__main__":
  30.     args = config.parse_args()
  31.     config = config.get_config(
  32.         args.config, overrides=args.override, show=False)
  33.     config.profiler_options = args.profiler_options
  34.     engine = Engine(config, mode="train")
  35.     
  36.     ## 初始化swanlab
  37.     swanlab.init(
  38.         experiment_name="Swanlab_ResNet50_PaddleClas",
  39.         description="Train ResNet50 for weeds classification.",
  40.         project="Swanhub_Weeds_Classification",
  41.         config={
  42.             "model": "ResNet50",
  43.             "optim": "Adam",
  44.             "lr": 0.001,
  45.             "batch_size": 64,
  46.             "num_epochs": 100,
  47.             "num_class": 9,
  48.         }
  49.     )
  50.     engine.train()

In PaddleClas, find ppcls-->engine-->train-->utils.py and add the following code:

  1. swanlab.log({"train_lr_msg": lr_msg.split(": ")[1]})  #
  2. swanlab.log({"train_CELoss": metric_msg.split(",")[0].split(': ')[1]})  ##
  3. swanlab.log({'train_loss': metric_msg.split(",")[1].split(': ')[1]})

In the PaddleClas folder, find ppcls-->engine-->engine.py and add the following code:

swanlab.log({'best_metric': best_metric.get('metric')})

3.3 Model Training

Enter the following command in the console:

python -m paddle.distributed.launch tools/train.py -c ./ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml

View experiment details at swanlab

The experimental results are as follows:

swanlab View experimental results

3.4 Model Reasoning

Enter the following code into the console:

python tools/infer.py -c ./ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml -o Infer.infer_imgs=./DeepWeeds/infer/01.jpg -o Global.pretrained_model=./output/Res2Net50_14w_8s/best_model

4. DarkNet53 model training

4.1 Modify configuration

First, find ppcls-->configs-->ImageNet-->DarkNet-->DarkNet53.yaml in the PaddleClas folder.

Modify epochs to 100, class class_num to 9, training image path, verification image path and label file. A total of 7 changes were made.

  1. # global configs
  2. Global:
  3.   checkpoints: null
  4.   pretrained_model: null
  5.   output_dir: ./output/
  6.   device: gpu
  7.   save_interval: 1
  8.   eval_during_train: True
  9.   eval_interval: 1
  10.   epochs: 100
  11.   print_batch_step: 10
  12.   use_visualdl: False
  13.   # used for static mode and model export
  14.   image_shape: [3, 256, 256]
  15.   save_inference_dir: ./inference
  16.  
  17. # model architecture
  18. Arch:
  19.   name: DarkNet53
  20.   class_num: 9
  21.  
  22. # loss function config for traing/eval process
  23. Loss:
  24.   Train:
  25.     - CELoss:
  26.         weight: 1.0
  27.         epsilon: 0.1
  28.   Eval:
  29.     - CELoss:
  30.         weight: 1.0
  31.  
  32.  
  33. Optimizer:
  34.   name: Momentum
  35.   momentum: 0.9
  36.   lr:
  37.     name: Cosine
  38.     learning_rate: 0.1
  39.   regularizer:
  40.     name: 'L2'
  41.     coeff: 0.0001
  42.  
  43.  
  44. # data loader for train and eval
  45. DataLoader:
  46.   Train:
  47.     dataset:
  48.       name: ImageNetDataset
  49.       image_root: F:/datasets/DeepWeeds/images
  50.       cls_label_path: F:/datasets/DeepWeeds/train.txt
  51.       transform_ops:
  52.         - DecodeImage:
  53.             to_rgb: True
  54.             channel_first: False
  55.         - RandCropImage:
  56.             size: 256
  57.         - RandFlipImage:
  58.             flip_code: 1
  59.         - NormalizeImage:
  60.             scale: 1.0/255.0
  61.             mean: [0.485, 0.456, 0.406]
  62.             std: [0.229, 0.224, 0.225]
  63.             order: ''
  64.       batch_transform_ops:
  65.         - MixupOperator:
  66.             alpha: 0.2
  67.  
  68.     sampler:
  69.       name: DistributedBatchSampler
  70.       batch_size: 64
  71.       drop_last: False
  72.       shuffle: True
  73.     loader:
  74.       num_workers: 4
  75.       use_shared_memory: True
  76.  
  77.   Eval:
  78.     dataset: 
  79.       name: ImageNetDataset
  80.       image_root: F:/datasets/DeepWeeds/images
  81.       cls_label_path: F:/datasets/DeepWeeds/val.txt
  82.       transform_ops:
  83.         - DecodeImage:
  84.             to_rgb: True
  85.             channel_first: False
  86.         - ResizeImage:
  87.             resize_short: 292
  88.         - CropImage:
  89.             size: 256
  90.         - NormalizeImage:
  91.             scale: 1.0/255.0
  92.             mean: [0.485, 0.456, 0.406]
  93.             std: [0.229, 0.224, 0.225]
  94.             order: ''
  95.     sampler:
  96.       name: DistributedBatchSampler
  97.       batch_size: 64
  98.       drop_last: False
  99.       shuffle: False
  100.     loader:
  101.       num_workers: 4
  102.       use_shared_memory: True
  103.  
  104. Infer:
  105.   infer_imgs: docs/images/inference_deployment/whl_demo.jpg
  106.   batch_size: 10
  107.   transforms:
  108.     - DecodeImage:
  109.         to_rgb: True
  110.         channel_first: False
  111.     - ResizeImage:
  112.         resize_short: 292
  113.     - CropImage:
  114.         size: 256
  115.     - NormalizeImage:
  116.         scale: 1.0/255.0
  117.         mean: [0.485, 0.456, 0.406]
  118.         std: [0.229, 0.224, 0.225]
  119.         order: ''
  120.     - ToCHWImage:
  121.   PostProcess:
  122.     name: Topk
  123.     topk: 5
  124.     class_id_map_file: F:/datasets/DeepWeeds/classnames
  125.  
  126. Metric:
  127.   Train:
  128.   Eval:
  129.     - TopkAcc:
  130.         topk: [1, 5]

4.2 Using Swanlab

Find tools->train.py in the PaddleClas folder. Modify the initialization of swanlab

  1. # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  2.  #
  3.  # Licensed under the Apache License, Version 2.0 (the "License");
  4.  # you may not use this file except in compliance with the License.
  5.  # You may obtain a copy of the License at
  6.  #
  7.  #     http://www.apache.org/licenses/LICENSE-2.0
  8.  #
  9.  # Unless required by applicable law or agreed to in writing, software
  10.  # distributed under the License is distributed on an "AS IS" BASIS,
  11.  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12.  # See the License for the specific language governing permissions and
  13.  # limitations under the License.
  14.  
  15. from __future__ import absolute_import
  16. from __future__ import division
  17. from __future__ import print_function
  18. import os
  19. import sys
  20.  
  21.  __dir__ = os.path.dirname(os.path.abspath(__file__))
  22.  sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
  23.  
  24. from ppcls.utils import config
  25. from ppcls.engine.engine import Engine
  26. import swanlab
  27. # -*- coding: utf-8 -*-
  28.  
  29. if __name__ == "__main__":
  30.     args = config.parse_args()
  31.     config = config.get_config(
  32.         args.config, overrides=args.override, show=False)
  33.     config.profiler_options = args.profiler_options
  34.     engine = Engine(config, mode="train")
  35.     
  36.     ## 初始化swanlab
  37.     swanlab.init(
  38.         experiment_name="Swanlab_DrakNet53_PaddleClas",
  39.         description="Train DarkNet53 for weeds classification.",
  40.         project="Swanhub_Weeds_Classification",
  41.         config={
  42.             "model": "DarkNet53",
  43.             "optim": "Adam",
  44.             "lr": 0.001,
  45.             "batch_size": 64,
  46.             "num_epochs": 100,
  47.             "num_class": 9,
  48.         }
  49.     )
  50.     engine.train()

4.3 Model Training

Enter the following command in the console:

python -m paddle.distributed.launch tools/train.py -c ./ppcls/configs/ImageNet/DarkNet/DarknetNet53.yaml

View experiment details at swanlab

The experimental results are as follows:

4.4 Model Reasoning

Enter the following code into the console:

python tools/infer.py -c ./ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml -o Infer.infer_imgs=./DeepWeeds/infer/01.jpg -o Global.pretrained_model=./output/DarkNet53/best_model

4.5 Swanlab Results Display

As can be seen from the figure, the effect of the ResNet50 model is better than that of the DarkNet53 model. Swanlab provides a convenient comparison chart function.

5. Gradio Demo

To be continued...

6. Swanhub uploads and demonstrates the demo

To be continued...

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