📦 Toy Box] Image Classification
This repository implements basic image classficiation using deeplearning.
You can download the dataset here .
It is connected through the wandb . You can check the simple result in here .
pytorch >= 1.6.0
timm == 0.4.9
albumentations == 1.0.0
openc-python == 4.5.2.54
wandb == 0.10.32
numpy == 1.20.3
scikit-learn
easydict
pyyaml
adamp
pandas
├─checkpoints
├─configs
├─data
├─debug_result
├─eda
├─fruits-360 (download dataset)
│ ├─papers
│ ├─Test
│ ├─test-multiple_fruits
│ ├─Training
├─prediction
├─results
└─src
├─lib
├─models
└─utils
You can set various experimental environments in configs/config.py
base :
seed : 42 # random seedmodel_arc : ' resnet18d' # you can use the model provided by timm.num_classes : 131
input_dir : ' ./data/train.csv' # dataframe generated from eda/labeling.ipynboutput_dir : ' ./checkpoints/' # path to save checkpointstrain_only : False # without validationcutmix_args : # for cutmix augmentation it will be improve to generalized performanceuse_cutmix : True
beta : 1.0
cutmix_prob : 0.5
train_args :
num_epochs : 5 # number of total epochstrain_batch_size : 128 # train mini-batch sizeval_batch_size : 128 # validation mini-batch sizeoptimizer : ' AdamP' # optimizermax_lr : 0.0001 # max learning rate for CosineAnnealingLRmin_lr : 0.00001 # min learning rate for CosineAnnealingLRcycle : 3 # total cyclegamma : 0.5 # restarts rateweight_decay : 0.0001 # weight decayscheduler : ' CosineAnnealingLR' # learning rate schedulerloss_fn : ' CrossEntropyLoss' # loss functionlog_intervals : 10 # steps for the print logeval_metric : ' accuracy' # evaluation metricval_args :
use_kfold : False # K-Fold Cross Validationn_splits : 0 # number of K (split size)test_size : 0.2 # validation set sizek-fold :
seed : 42
model_arc : ' resnet18d' num_classes : 131
input_dir : ' ./data/train.csv' output_dir : ' ./checkpoints/' train_only : False
cutmix_args :
use_cutmix : True
beta : 1.0
cutmix_prob : 0.5
train_args :
num_epochs : 1
train_batch_size : 128
val_batch_size : 128
optimizer : ' AdamP' max_lr : 0.0001
min_lr : 0.00001
cycle : 3
gamma : 0.5
weight_decay : 0.0001
scheduler : ' CosineAnnealingLR' loss_fn : ' CrossEntropyLoss' log_intervals : 10
eval_metric : ' accuracy' val_args :
use_kfold : True
n_splits : 5
test_size : 0.0
run eda/labeling.ipynb
python main.py
python inference.pypython eval.py
Accuracy : 0.96602
F1 Score : 0.96460
'*==================Classificaion Report==================*'
precision recall f1-score support
Apple Braeburn 0.95 0.96 0.95 164
Apple Crimson Snow 0.91 0.99 0.95 148
Apple Golden 1 1.00 1.00 1.00 160
Apple Golden 2 1.00 1.00 1.00 164
Apple Golden 3 0.92 1.00 0.96 161
Apple Granny Smith 1.00 0.91 0.96 164
Apple Pink Lady 0.97 1.00 0.98 152
Apple Red 1 0.84 0.95 0.89 164
Apple Red 2 0.95 0.95 0.95 164
Apple Red 3 0.97 1.00 0.98 144
Apple Red Delicious 1.00 1.00 1.00 166
Apple Red Yellow 1 1.00 0.99 0.99 164
Apple Red Yellow 2 1.00 1.00 1.00 219
Apricot 0.95 1.00 0.98 164
Avocado 0.99 0.99 0.99 143
Avocado ripe 0.99 1.00 1.00 166
Banana 0.98 1.00 0.99 166
Banana Lady Finger 1.00 0.89 0.94 152
Banana Red 1.00 1.00 1.00 166
Beetroot 0.88 0.85 0.86 150
Blueberry 0.96 0.99 0.97 154
Cactus fruit 1.00 1.00 1.00 166
Cantaloupe 1 1.00 1.00 1.00 164
Cantaloupe 2 1.00 1.00 1.00 164
Carambula 1.00 1.00 1.00 166
Cauliflower 0.82 1.00 0.90 234
Cherry 1 1.00 1.00 1.00 164
Cherry 2 1.00 1.00 1.00 246
Cherry Rainier 1.00 1.00 1.00 246
Cherry Wax Black 1.00 1.00 1.00 164
Cherry Wax Red 1.00 1.00 1.00 164
Cherry Wax Yellow 1.00 1.00 1.00 164
Chestnut 0.94 0.97 0.95 153
Clementine 1.00 1.00 1.00 166
Cocos 1.00 0.99 1.00 166
Corn 1.00 0.42 0.59 150
Corn Husk 0.88 0.66 0.75 154
Cucumber Ripe 1.00 0.92 0.96 130
Cucumber Ripe 2 1.00 0.90 0.95 156
Dates 0.99 1.00 0.99 166
Eggplant 1.00 0.80 0.89 156
Fig 1.00 1.00 1.00 234
Ginger Root 0.88 1.00 0.94 99
Granadilla 1.00 1.00 1.00 166
Grape Blue 0.98 1.00 0.99 328
Grape Pink 0.95 1.00 0.98 164
Grape White 1.00 1.00 1.00 166
Grape White 2 1.00 1.00 1.00 166
Grape White 3 1.00 0.89 0.94 164
Grape White 4 0.90 1.00 0.95 158
Grapefruit Pink 1.00 1.00 1.00 166
Grapefruit White 1.00 1.00 1.00 164
Guava 0.90 1.00 0.95 166
Hazelnut 1.00 1.00 1.00 157
Huckleberry 1.00 1.00 1.00 166
Kaki 1.00 1.00 1.00 166
Kiwi 1.00 1.00 1.00 156
Kohlrabi 0.83 1.00 0.90 157
Kumquats 1.00 1.00 1.00 166
Lemon 0.97 1.00 0.98 164
Lemon Meyer 1.00 1.00 1.00 166
Limes 0.99 1.00 1.00 166
Lychee 1.00 1.00 1.00 166
Mandarine 1.00 1.00 1.00 166
Mango 1.00 1.00 1.00 166
Mango Red 0.96 0.94 0.95 142
Mangostan 1.00 1.00 1.00 102
Maracuja 0.90 1.00 0.95 166
Melon Piel de Sapo 1.00 1.00 1.00 246
Mulberry 1.00 1.00 1.00 164
Nectarine 0.75 1.00 0.85 164
Nectarine Flat 0.98 0.93 0.96 160
Nut Forest 0.98 0.98 0.98 218
Nut Pecan 0.96 0.99 0.98 178
Onion Red 1.00 1.00 1.00 150
Onion Red Peeled 0.99 1.00 1.00 155
Onion White 0.99 1.00 0.99 146
Orange 1.00 1.00 1.00 160
Papaya 1.00 1.00 1.00 164
Passion Fruit 0.96 0.95 0.95 166
Peach 1.00 0.66 0.79 164
Peach 2 1.00 1.00 1.00 246
Peach Flat 0.91 1.00 0.95 164
Pear 0.99 1.00 1.00 164
Pear 2 1.00 0.56 0.71 232
Pear Abate 1.00 1.00 1.00 166
Pear Forelle 0.69 1.00 0.82 234
Pear Kaiser 1.00 1.00 1.00 102
Pear Monster 1.00 0.99 1.00 166
Pear Red 1.00 1.00 1.00 222
Pear Stone 0.91 1.00 0.95 237
Pear Williams 1.00 1.00 1.00 166
Pepino 1.00 0.79 0.88 166
Pepper Green 1.00 1.00 1.00 148
Pepper Orange 1.00 1.00 1.00 234
Pepper Red 0.74 0.92 0.82 222
Pepper Yellow 1.00 1.00 1.00 222
Physalis 1.00 1.00 1.00 164
Physalis with Husk 0.90 1.00 0.95 164
Pineapple 0.99 1.00 1.00 166
Pineapple Mini 1.00 1.00 1.00 163
Pitahaya Red 1.00 1.00 1.00 166
Plum 0.99 1.00 0.99 151
Plum 2 0.99 1.00 0.99 142
Plum 3 1.00 1.00 1.00 304
Pomegranate 0.93 0.80 0.86 164
Pomelo Sweetie 1.00 1.00 1.00 153
Potato Red 1.00 0.69 0.81 150
Potato Red Washed 1.00 0.95 0.98 151
Potato Sweet 0.93 0.63 0.75 150
Potato White 0.79 1.00 0.88 150
Quince 1.00 1.00 1.00 166
Rambutan 1.00 1.00 1.00 164
Raspberry 0.99 1.00 1.00 166
Redcurrant 1.00 1.00 1.00 164
Salak 0.93 1.00 0.96 162
Strawberry 1.00 1.00 1.00 164
Strawberry Wedge 1.00 1.00 1.00 246
Tamarillo 0.99 1.00 0.99 166
Tangelo 1.00 1.00 1.00 166
Tomato 1 1.00 0.99 0.99 246
Tomato 2 1.00 1.00 1.00 225
Tomato 3 0.92 1.00 0.96 246
Tomato 4 1.00 1.00 1.00 160
Tomato Cherry Red 1.00 1.00 1.00 164
Tomato Heart 1.00 0.69 0.82 228
Tomato Maroon 1.00 1.00 1.00 127
Tomato Yellow 1.00 1.00 1.00 153
Tomato not Ripened 1.00 1.00 1.00 158
Walnut 0.92 1.00 0.96 249
Watermelon 0.98 1.00 0.99 157
accuracy 0.97 22688
macro avg 0.97 0.97 0.96 22688
weighted avg 0.97 0.97 0.96 22688
