Weed Classification

Weed Classification/Dataset/README.md

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+# Weed Classification using DL 
+
+## PROJECT TITLE
+
+Weed Detection using Deep Learning 
+
+## GOAL
+
+To identify the weed image. 
+
+## DATASET
+
+The link for the dataset used in this project:  https://www.kaggle.com/datasets/imsparsh/deepweeds
+It has 9 classes of Classification
+
+## EDA:
+![EDA](../Images/EDA1.png)
+![Dataset Sample](../Images/Input.png)
+
+## DESCRIPTION
+
+This project aims to identify the weed name using Deep Learning.
+
+## WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above using TensorflowDataset. 
+2. Data preprocessing: Preprocessed the image according to the requirement of the model.
+3. Model selection: Densenet and Mobilnet V2 with a added Dense Classification Layer
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+
+## MODELS SUMMARY
+
+Model: "model" Densenet
+__________________________________________________________________________________________________
+ Layer (type)                   Output Shape         Param #     Connected to                     
+==================================================================================================
+ input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
+                                )]                                                                
+
+ zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0          ['input_1[0][0]']                
+
+ conv1/conv (Conv2D)            (None, 112, 112, 64  9408        ['zero_padding2d[0][0]']         
+                                )                                                                 
+
+ conv1/bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1/conv[0][0]']             
+                                )                                                                 
+
+ conv1/relu (Activation)        (None, 112, 112, 64  0           ['conv1/bn[0][0]']               
+                                )                                                                 
+
+ zero_padding2d_1 (ZeroPadding2  (None, 114, 114, 64  0          ['conv1/relu[0][0]']             
+ D)                             )                                                                 
+
+ pool1 (MaxPooling2D)           (None, 56, 56, 64)   0           ['zero_padding2d_1[0][0]']       
+
+ conv2_block1_0_bn (BatchNormal  (None, 56, 56, 64)  256         ['pool1[0][0]']                  
+ ization)                                                                                         
+...
+Total params: 7,333,961
+Trainable params: 380,105
+Non-trainable params: 6,953,856
+
+Model: "sequential_1" Mobilenet
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ mobilenetv2_1.00_224 (Funct  (None, 8, 8, 1280)       2257984   
+ ional)                                                          
+
+ global_average_pooling2d (G  (None, 1280)             0         
+ lobalAveragePooling2D)                                          
+
+ dense_3 (Dense)             (None, 256)               327936    
+
+ dropout_1 (Dropout)         (None, 256)               0         
+
+ dense_4 (Dense)             (None, 9)                 2313      
+
+=================================================================
+Total params: 2,588,233
+Trainable params: 330,249
+Non-trainable params: 2,257,984
+_________________________________________________________________
+
+## LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- matplotlib
+- tensorflow
+- keras
+- PIL
+
+## EVALUATION METRICS
+
+The evaluation metrics I used to assess the models:
+
+- Accuracy 
+- Loss
+- Confusion Matrix
+
+It is shown using Confusion Matrix in the Images folder
+
+## RESULTS
+Results on Val dataset:
+For Mobilnet:
+Accuracy:83%
+loss: 0.47
+
+For Model-2:
+Accuracy:70%
+loss: 0.82
+
+
+## CONCLUSION
+Based on results we can draw following conclusions:
+
+1.The densenet model worked better than the mobilenet model

Weed Classification/README.md

@@ -0,0 +1,119 @@
+# Weed Classification using DL 
+
+## PROJECT TITLE
+
+Weed Detection using Deep Learning 
+
+## GOAL
+
+To identify the weed image. 
+
+## DATASET
+
+The link for the dataset used in this project:  https://www.kaggle.com/datasets/imsparsh/deepweeds
+It has 9 classes of Classification
+
+## EDA:
+![EDA](Images/EDA1.png)
+![Dataset Sample](Images/Input.png)
+
+## DESCRIPTION
+
+This project aims to identify the weed name using Deep Learning.
+
+## WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above using TensorflowDataset. 
+2. Data preprocessing: Preprocessed the image according to the requirement of the model.
+3. Model selection: Densenet and Mobilnet V2 with a added Dense Classification Layer
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+
+## MODELS SUMMARY
+
+Model: "model" Densenet
+__________________________________________________________________________________________________
+ Layer (type)                   Output Shape         Param #     Connected to                     
+==================================================================================================
+ input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
+                                )]                                                                
+
+ zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0          ['input_1[0][0]']                
+
+ conv1/conv (Conv2D)            (None, 112, 112, 64  9408        ['zero_padding2d[0][0]']         
+                                )                                                                 
+
+ conv1/bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1/conv[0][0]']             
+                                )                                                                 
+
+ conv1/relu (Activation)        (None, 112, 112, 64  0           ['conv1/bn[0][0]']               
+                                )                                                                 
+
+ zero_padding2d_1 (ZeroPadding2  (None, 114, 114, 64  0          ['conv1/relu[0][0]']             
+ D)                             )                                                                 
+
+ pool1 (MaxPooling2D)           (None, 56, 56, 64)   0           ['zero_padding2d_1[0][0]']       
+
+ conv2_block1_0_bn (BatchNormal  (None, 56, 56, 64)  256         ['pool1[0][0]']                  
+ ization)                                                                                         
+...
+Total params: 7,333,961
+Trainable params: 380,105
+Non-trainable params: 6,953,856
+
+Model: "sequential_1" Mobilenet
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ mobilenetv2_1.00_224 (Funct  (None, 8, 8, 1280)       2257984   
+ ional)                                                          
+
+ global_average_pooling2d (G  (None, 1280)             0         
+ lobalAveragePooling2D)                                          
+
+ dense_3 (Dense)             (None, 256)               327936    
+
+ dropout_1 (Dropout)         (None, 256)               0         
+
+ dense_4 (Dense)             (None, 9)                 2313      
+
+=================================================================
+Total params: 2,588,233
+Trainable params: 330,249
+Non-trainable params: 2,257,984
+_________________________________________________________________
+
+## LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- matplotlib
+- tensorflow
+- keras
+- PIL
+
+## EVALUATION METRICS
+
+The evaluation metrics I used to assess the models:
+
+- Accuracy 
+- Loss
+- Confusion Matrix
+
+It is shown using Confusion Matrix in the Images folder
+
+## RESULTS
+Results on Val dataset:
+For Mobilnet:
+Accuracy:83%
+loss: 0.47
+
+For Model-2:
+Accuracy:70%
+loss: 0.82
+
+
+## CONCLUSION
+Based on results we can draw following conclusions:
+
+1.The densenet model worked better than the mobilenet model