This is a university project for the course "Computer Vision". This project consists of a classifier of a car model.
- Python3
- numpy
- pytorch
- torchvision
- scikit-learn
- matplotlib
- pillow
- torch (pytorch)
- torchvision
You can install the requirements using:
pip3 install -r requirements.txt
Troubleshooting: if you get some errors about pytorch or torchvision install use sudo to install it.
First, if you have no resnet152 model trained and you need from scratch to do it you need to:
- download dataset
- preprocess the dataset
- train the model
Afterward you can try a new sample.
I suggest using VMMRdb as a dataset, it's free and full of labeled images for car model recognition instead of detection (most datasets are for this).
So download the dataset, select some models and put the directory model in the dataset folder, any directory in "dataset" will be considered a new class.
If you need more data for your project you can also add the followings dataset:
- Stanford Cars Dataset from jkrause (low images quantity)
- Comprehensive Cars Database, here the module to get this dataset MODULE
The dataset structure should be like this:
dataset / classes / file.jpg
For example, we have 3 classes: honda_civic, nissan and ford:
dataset_dir / honda_civic / file1.jpg
dataset_dir / honda_civic / file2.jpg
....
dataset_dir / nissan / file1.jpg
dataset_dir / nissan / file2.jpg
....
dataset_dir / ford / file1.jpg
dataset_dir / ford / file2.jpg
...
and so on.
The "dataset_dir" is the IMAGES_PATH in config.py. The python script will save the classes in a dict() named num_classes, like this:
num_classes = {
"honda_civic": 1,
"nissan": 2,
"ford": 3
}
This conversion happens automatically when you just add a directory inside the IMAGES_PATH, if you add tomorrow a new car, like, FIAT, the program will add automatically to the classes, just pay attention to the order of the classes inside num_classes and the related training, testing and validation CSV files.
The file training, testing, and validation (CSV) should contain only two columns: FILE_NAME, NUM_CLASS
Example of CSV file:
file1.jpg, 1
file2.jpg, 1
file1.jpg, 2
file2.jpg, 2
file1.jpg, 3
file2.jpg, 3
Anyway, this paragraph is only for your info, the CSV files are automatically generated by the preprocessing phase explained in the following paragraph.
You have to generate the CSV files and calculate the mean and standard deviation to apply a normalization, just use the -p parameter to process your dataset so type:
$ python3 main.py -p
Short introduction
Before the training process, modify the EPOCHS parameter in config.py, usually with 3 classes 30-50 epochs should be enough, but you have to see the results_graph.png file (when you finish your training with the default epochs parameter) and check if the blue curve is stable.
An example of the graph could be the following:
After 45-50 epochs (number bottom of the graph), the blue curve is stable and does not have peaks down. Moreover, the testing curve (the orange one) is pretty "stable", even with some peaks, for the testing is normal that the peaks are frequently.
Train the model
To train a new model resnet152 model you can run the main.py with the -t parameter, so type:
$ python3 main.py -t
The results will be saved in the results/ directory with the F1 score, accuracy, confusion matrix, and the accuracy/loss graph difference between training and testing.
Try to predict a new sample you can just type:
python3 main.py -i path/file.jpg
I used this project to predict 3 models:
- Nissan Altima
- Honda Civic
- Ford Explorer
I selected all 2000-2007 images from VMMRdb, so I downloaded the full dataset and choose the 2000-2007 images and put them into one directory per class (so I had 3 directories named "Ford Explorer", "Nissan Altima", "Honda Civic" in dataset folder).
Error:
RuntimeError: Error(s) in loading state_dict for ResNet:
size mismatch for fc.weight: copying a param with shape torch.Size([1000, 2048]) from checkpoint, the shape in current model is torch.Size([3, 2048]).
size mismatch for fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([3]).Solution: you probably need to re-train your neural network model because you are using the wrong model for your data and classes, so don't use some pretrained model but train a new neural network with your data/classes.
Error:
######### ERROR #######
CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.96 GiB total capacity; 967.98 MiB already allocated; 25.94 MiB free; 48.02 MiB cached)
######### batch #######
[images.png, files_path.png, ....]
Traceback (most recent call last):
File "main.py", line 227, in <module>
train_model_iter("resnet152", resnet152_model)
File "main.py", line 215, in train_model_iter
model, loss_acc, y_testing, preds = train_model(model_name=model_name, model=model, weight_decay=weight_decay)
File "main.py", line 124, in train_model
epoch_loss /= samples
ZeroDivisionError: division by zeroSolution: you're using CUDA, probably, the memory of your GPU is too low for the batch size that you're giving in input, try to reduce the BATCH_SIZE from config.py or use your RAM instead of GPU memory if you have more, so put USE_CUDA=false in config.py.
Probably you have to increase the DATA PER CLASS in your dataset, a good number of images per class could be 10k (10 000 items), but with only 3 classes you can even use 2k-5k items per class. Another parameter that affects hugely the training is the EPOCHS, try to at least 50 epochs if you are not satisfied with the results.
You are not the only one to get these troubles, check the issue #3 to get a full conversation of this solutions/troubleshooting.
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