Image-Classification-Resnet34

Introduction:

By using model Resnet-18 which was built by myself, I used Pytorch to classify image of 10 animal types.

Here is my pytorch implementation of the model described in the RESNET paper.

Note: Because this is the model I built, instead of this is trained by COCO dataset, I trained by dataset of 10 animal classes, which I present later.

Dataset:

Statistics of datasets I used for experiments. These datasets could be download from link

Classes	Train samples	Test samples
butterfly	1902	210
cat	1508	160
chicken	2790	308
cow	1684	182
dog	4373	490
elephant	1306	140
horse	2357	266
sheep	1638	182
spider	4345	476
squirre	1680	182

Settings:

For optimizer and learning rate, I use:

• SGD optimizer with different learning rates (0.01 in most cases).

Additionally, in the my model, I will set up 100 epochs (using early stopping if after 5 epochs, if there is not greater score, it will stop train proccess) ,which is seen as a loop over batch_size: 16

Training:

If you want to train a model with default parameters, you could run:

python train_animal.py If you want to adjust your preference parameters, here is some option you can choose:

Parameters	Abbreviation	Default	Description
--batch-size	-b	16	Select suitable batch size
--data-path	-p	'../../'	directory contains dataset
--lr		1e-2	modify learning rate
--epochs	-e	100	modify epoch number
--log-path	-l	tensorboard	directory contains metrics visualization
--checkpoint	-sc	tensorboard/animals/epoch_last.pt	directory which saves the train model

For example: python train.py -p dataset_location --log-patch directory-name

How to view tensorboard:

 tensorboard --logdir directory/contain/tensorboard/

Evaluating:

You could preview my evaluating process throughout Tenserboard. After 22 epochs, I reached:

• The highest accuracy score: 0.7 in epoch 19

• Confusion matrix:

• Cross entropy loss in 2 processes:

Testing:

After buiding models, I started to implement the testing process by file test_cnn.py

Regarding to the above confusion matrix, this model had good prediction in 4 classes: spider, chicken, horse and butterfly.

In contrast, it scored the worst prediction in class: Cat.