imagenette_experiments

experiments with fastai imagenette / imagewoof datasets

This repo for store results of experiments with Imagenette / Imagewoof datasets.

First "BATCH" of experiments stores at folder: Imagenette_Nbs_1
Four notebooks (Names stored with "Woof") leaved at "root" as there is urls from forums to it.
That experiments are with fastai v1.

Experiment results vs results on leaderboard at publish day.
ImageWOOF dataset:

Size (px)	Epochs		Accuracy	# Runs	My res	URL	Comments
128	5		73.37%	5, mean
128	20		85.52%	5, mean	86.10%
128	80		87.20%	1	87.63%	notebook	3 runs, start_pct=0.3
128	200		87.20%	1	88.30%	notebook	3 runs, start_pct=0.2
192	5		75.94%	5, mean	77.87%	notebook	added to board
192	20		87.25%	5, mean	87.85%	notebook	added to board
192	80		89.21%	1	89.69%	notebook	4 runs.
192	200		89.54%	1	90.35%	notebook	2 runs.
256	5		76.87%	5, mean	78,84%	notebook	added to board
256	20		88.29%	5, mean	88,58%	notebook	added to board
256	80		90.48%	1	90.63%	notebook	2 runs, start_pct=0.4
256	200		90.38%	1	91.14%	notebook	3 runs, start_pct=0.2

This results was done with experimental model - XResnet with modification.
I used pool layer plus convolution stride 1 instead of convolution stride 2.
And instead of regular pytorch pool (AveragePool2d and MaxPool2d) i used MaxBlurPool as described here:
fastai forum topic
github ducha-aiki

Activation function - Mish, long disscussion on fastai forum
Fit with Ranger optimizer and flat with annealing - long tread on fastai forum

Model was created with model-constructor
Explanation how model was created here: notebook: ResnetTrick_create_model_fit.ipynb

That experiments was with fastai version v1, explanation on page "First version".
All notebooks can be run on google colab.