hrsht-13 / Leaf_Classification-Pytorch

Leaf-Classification using Pytorch with Custom Training loop and Transfer Learning, achieved a validation accuracy of 0.897 .

Leaf_Classification-Pytorch

Building a model using Trasfer Learning through Pytorch with Costum training loop.

Dataset

It contains all 185 tree species from the Northeastern United States. Dataset is highly imbalanced, as the lowest number of images a species has is around 20 images, while the highest number of images is around 150 images. All the images are in RGB.

Preprocessing of Data

Preprocessing of data done through skimage library using transforms.

1. Resizing
2. CentreCropping
3. Normalizing the images
4. LabelEncoding the Target values

Splitting the Dataset

Dataset has been splitted in Train and Val set in the ratio of 2:3 and 1:3 respectively.

Model

Resnet-18 has been used with Cross entropy loss and SGD optimizer. Further Learning rate reducer has been used to control the rate or speed at which the model learns.

Results

The model achieved :

1. train-acc of 98.94% with a val-acc of 89.73%
2. train-loss of 0.111 with a val-loss if 0.357