The model reaches a maximum accuracy of 55.42% on Tiny-ImageNet using ResNet 18 model.
- Loss Function: Cross Entropy Loss (combination of
nn.LogSoftmax
andnn.NLLLoss
) - Optimizer: SGD
- Momentum: 0.9
- Learning Rate: 0.01
- Reduce LR on Plateau
- Patience: 2
- Factor: 0.1
- Min LR: 1e-6
- Epochs: 50
- Batch Size: 128
The following data augmentation techniques were applied to the dataset during training:
- Horizontal Flip
- Vertical Flip
- Random Rotate
- CutOut
Finding anchor boxes on a dataset of 50 dogs using K-Means Clustering ALgorithm. The dataset was annotated and exported in COCO JSON Format. It can be found here.
After running the algorithm on the dataset, it was found that the best k can have the value of 3 or 4.
Number of Clusters (k) | Mean IoU | Cluster Plot | Anchor Boxes |
---|---|---|---|
3 | 0.75 | ||
4 | 0.79 |
Install the required packages
$ pip install -r requirements.txt
Upload the directory tensornet
and the file dogs.json
in the root folder and select Python 3 as the runtime type and GPU as the harware accelerator.
- Shantanu Acharya (Canvas ID: 25180630)
- Rakhee (Canvas ID: 25180625)