Step 1: Download the data folder which contains the pre-processed dataset Step 2: Update the path of the data folder in the Multi_Classification_Vehicle.ipynb file Step 3: Run the Multi_Classification_Vehicle.ipynb file Step 4: Update the path to save the model
Step 1: Update the path of the model (trained model file can be used model_saved_4_15.h5) in the output_final.ipynb file Step 2: Download the test images Step 3: Update the path of the test images in the output_final.ipynb file Step 4: Run the output_final.ipynb file
Step 1: Download the dataset folder which contains the pre-processed dataset and run the first cell in tutorial.ipynb to git clone the yolov5 environment Step 2: Make a .yaml file containing the path of the dataset folder and put the yaml file in the data folder for example path: D:\Sem-5\LAB\ANPR\yolov5\data\pre960 # dataset root dir train: train\images\ # train images (relative to 'path') val: train\images\ # val images (relative to 'path') test: test\images\ # test images (optional) # Classes names: 0: number_plate Step 3: Update the path of the .yaml file in the tutorial.ipynb in the training cell Step 4: Run the training cell Step 5: Update the path of the trained model in the Detect cell the path to the weights will be printed out by the training cell Step 6: Add Car image path in the Detect cell Step 7: Run the Detect cell Step 8: If foreign car go to foreign car cell else Indian car cell update the exp# number as shown in the output of the Detect cell Step 9: Run the Indian car cell or foreign car cell