SOLUTION WRITE UP -

https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/108030

Kaggle-APTOS-2019-Blindness-Detection

About Challange:

Imagine being able to detect blindness before it happened.

Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working aged adults. Aravind Eye Hospital in India hopes to detect and prevent this disease among people living in rural areas where medical screening is difficult to conduct. Successful entries in this competition will improve the hospital’s ability to identify potential patients. Further, the solutions will be spread to other Ophthalmologists through the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium

Currently, Aravind technicians travel to these rural areas to capture images and then rely on highly trained doctors to review the images and provide diagnosis. Their goal is to scale their efforts through technology; to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.

In this synchronous Kernels-only competition, you'll build a machine learning model to speed up disease detection. You’ll work with thousands of images collected in rural areas to help identify diabetic retinopathy automatically. If successful, you will not only help to prevent lifelong blindness, but these models may be used to detect other sorts of diseases in the future, like glaucoma and macular degeneration.

SETUP

OLD DATA - Diabetic Retinopathy Detection (https://www.kaggle.com/c/diabetic-retinopathy-detection)
NEW DATA - APTOS 2019 Blindness Detection (https://www.kaggle.com/c/aptos2019-blindness-detection/data)

EXP_725 (LB: 0.808)

I am gonna first pretrain model on OLD DATA and than fine tune on NEW DATA with 5-fold cross-validaion Important transformation here is zoom crop to the center from (0.9 to 1.4)

EXP_725.ipynb

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              128
SZ:              224
VALID:           NEW DATA

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(0.9, 1.4), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(10, 1e-3,   wd=1e-2, div_factor=10, pct_start=0.3)-UNF
                 fit_one_cycle(5,  1e-3/5, wd=1e-2, div_factor=10, pct_start=0.3)-UNF
                 fit_one_cycle(30, 1e-3/8, wd=1e-2, div_factor=10, pct_start=0.3)-UNF
MODEL WEIGHTS:   NB_EXP_725_UNFREEZE_P3
MODEL TRN_LOSS:  0.305515
MODEL VAL_LOSS:  0.342098
QUADR KAPPA:     0.887489
LB SCORE:        0.725
SUBMISSION FLN:  EXP_725(version 11/14)

Comments: Pretrained model trained just OLD DATA gives pretty good results. Now using best weight to fine tune new data

[EXP_725-CV_0 - EXP_725-CV_4].ipynb

Using weights NB_EXP_725_UNFREEZE_P3To train NEW DATA with 5 fold splits.

Set up for all CV experimetns:

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              128
SZ:              224
VALID:           NEW DATA CV SPLIT

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(0.9, 1.4), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(15, 1e-3,   wd=1e-2, div_factor=10, pct_start=0.3)-UNF

Summary:

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_725-CV_0	0.303028	0.198670	0.924300	NB_EXP_725_CV_0_UNFREEZE_P1
EXP_725-CV_1	0.259711	0.261448	0.909077	NB_EXP_725_CV_1_UNFREEZE_P1
EXP_725-CV_2	0.318001	0.231378	0.914873	NB_EXP_725_CV_2_UNFREEZE_P1
EXP_725-CV_3	0.201959	0.182800	0.929271	NB_EXP_725_CV_3_UNFREEZE_P1
EXP_725-CV_4	0.195760	0.204893	0.937953	NB_EXP_725_CV_4_UNFREEZE_P1

Submission (Average all the predictions)

LB SCORE:        0.808
SUBMISSION FLN:  EXP_725(version 12/14)

EXP_725_352 (LB: 0.785)

Same as EXP_725 but increased image size to 352 and added more robust center zoom crop (1.1 - 1.45x). Trained using weights from EXP_725, NB_EXP_725_UNFREEZE_P3

EXP_725_352.ipynb

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              56
SZ:              352
VALID:           NEW DATA

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.1, 1.45), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(5, 1e-3,    wd=1e-2, div_factor=10, pct_start=0.3)-UNF
                 fit_one_cycle(3, 1e-3/10, wd=1e-2, div_factor=25, pct_start=0.3)-UNF

MODEL WEIGHTS:   NB_EXP_725_352_UNFREEZE_P2
MODEL TRN_LOSS:  0.289148
MODEL VAL_LOSS:  0.335972
QUADR KAPPA:     0.893238
LB SCORE:        0.727
SUBMISSION FLN:  EXP_725_352(version 16/17)

Comments: Pretrained model on NB_EXP_725_UNFREEZE_P3 with image siae 224, Increaseing image size to 352 and adding extra zoom helped with the validation loss.

[EXP_725_352-CV_0 - EXP_725_352-CV_4].ipynb

Using weights NB_EXP_725_UNFREEZE_P3To train NEW DATA with 5 fold splits.

Set up for all CV experimetns:

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              56
SZ:              352
VALID:           NEW DATA CV SPLIT

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.1, 1.45), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(15, 1e-3,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Summary:

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_725_352-CV_0	0.214058	0.191476	0.926447	NB_EXP_725_352_CV_0_UNFREEZE_P1
EXP_725_352-CV_1	0.220699	0.232317	0.914668	NB_EXP_725_352_CV_1_UNFREEZE_P1
EXP_725_352-CV_2	0.216604	0.218829	0.921627	NB_EXP_725_352_CV_2_UNFREEZE_P1
EXP_725_352-CV_3	0.222879	0.165061	0.931339	NB_EXP_725_352_CV_3_UNFREEZE_P1
EXP_725_352-CV_4	0.218691	0.189928	0.936874	NB_EXP_725_352_CV_4_UNFREEZE_P1

Submission (Average all the predictions)

LB SCORE:        0.785
SUBMISSION FLN:  EXP_725_352(version 15/15)

EXP_730_BEN (LB: 0.804)

Same as EXP_725 added more robust center zoom crop (1.02 - 1.35x). Trained using weights from EXP_725, NB_EXP_725_UNFREEZE_P3. Images this time were proces. with Ben Method. Old ben method was not taking in two condiseration image ratio when resizing, I have added function resize_to which preserves image ratio when resizing. For proceessing images I used notebook BEN_PROCESS.ipynb

EXP_730.ipynb

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              128
SZ:              224
VALID:           NEW DATA

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.02, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(10, 1e-3,    wd=1e-2, div_factor=25, pct_start=0.3)-UNF

MODEL WEIGHTS:   NB_EXP_725_352_UNFREEZE_P2
MODEL TRN_LOSS:  0.316414
MODEL VAL_LOSS:  0.302961
QUADR KAPPA:     0.896701
LB SCORE:        0.755
SUBMISSION FLN:  EXP_725_352(version 17/17)

Comments: Model trained using old data and weights from EXP_725, showed good training and loss.

[EXP_730-CV_0 - EXP_730-CV_4].ipynb

Using weights NB_EXP_730_UNFREEZE_P1To train NEW DATA with 5 fold splits.

Set up for all CV experimetns:

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              56
SZ:              352
VALID:           NEW DATA CV SPLIT

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                  zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(15, 1e-3,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Summary:

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_730-CV_0	0.230880	0.214356	0.920836	NB_EXP_730_CV_0_UNFREEZE_P1
EXP_730-CV_1	0.298317	0.251441	0.918218	NB_EXP_730_CV_1_UNFREEZE_P1
EXP_730-CV_2	0.216604	0.299689	0.904481	NB_EXP_730_CV_2_UNFREEZE_P1
EXP_730-CV_3	0.178483	0.176829	0.932204	NB_EXP_730_CV_3_UNFREEZE_P1
EXP_730-CV_4	0.170133	0.235239	0.928617	NB_EXP_730_CV_4_UNFREEZE_P1

Submission (Average all the predictions)

LB SCORE:        0.804
SUBMISSION FLN:  EXP_730_BEN(version 22/22)

EXP_730_352_BEN (LB: TBD)

Same as EXP_730_BEN added more robust center zoom crop (1.02 - 1.35x) and the image size increased to 352. Trained using weights from EXP_730_BEN, NB_EXP_730_UNFREEZE_P1.

EXP_730_352.ipynb

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              56
SZ:              352
VALID:           NEW DATA

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.02, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(5, 1e-3/2,    wd=1e-2, div_factor=25, pct_start=0.3)-UNF

MODEL WEIGHTS:   NB_EXP_730_352_UNFREEZE_P1
MODEL TRN_LOSS:  0.224072
MODEL VAL_LOSS:  0.342114
QUADR KAPPA:     0.890448
LB SCORE:        TBD
SUBMISSION FLN:  TBD

Comments: This is trained mainly to use for transfer learning

EXP_735 (LB: 0.793)

Training on old data with image size 352, I am using weitts for transfer learning from the notebook EXP_730_352, NB_EXP_730_352_UNFREEZE_P1. Image were first cropped to remove all the black background using script TBD.

EXP_735.ipynb

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              56
SZ:              352
VALID:           NEW DATA

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.02, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(10, 1e-3,    wd=1e-2, div_factor=25, pct_start=0.3)-UNF

MODEL WEIGHTS:   NB_EXP_735_UNFREEZE_P1
MODEL TRN_LOSS:  0.233254
MODEL VAL_LOSS:  0.332348
QUADR KAPPA:     0.893338
LB SCORE:        TBD
SUBMISSION FLN:  TBD

Comments: Looks good move to cv

[EXP_735-CV_0 - EXP_735-CV_4].ipynb

Using weights NB_EXP_735_UNFREEZE_P1To train NEW DATA with 5 fold splits.

Set up for all CV experimetns:

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              56
SZ:              352
VALID:           NEW DATA CV SPLIT

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                  zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(15, 1e-3,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Summary:

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_735-CV_0	0.250133	0.205121	0.920307	NB_EXP_735_CV_0_UNFREEZE_P1
EXP_735-CV_1	0.135174	0.211053	0.921838	NB_EXP_735_CV_1_UNFREEZE_P1
EXP_735-CV_2	0.176270	0.170685	0.932106	NB_EXP_735_CV_2_UNFREEZE_P1
EXP_735-CV_3	0.230097	0.223343	0.915626	NB_EXP_735_CV_3_UNFREEZE_P1
EXP_735-CV_4	0.146527	0.205450	0.931362	NB_EXP_735_CV_4_UNFREEZE_P1

Submission (Average all the predictions)

LB SCORE:        0.793
SUBMISSION FLN:  EXP_352_crop(version 25/25)

EXP_740 (LB: 0.821, PB: 0.926)

In this experiment I combine OLD DATA with NEW DATA and do StratifiedKFold 5 Fold CV. Before combining I remove in NEW DATA all the duplicates and confusing label images (see in notebooks function get_ign_list). Moreover images are preprocessed using PROCCES.ipynb. This processing helps to remove extra black baground and center the images. Training is done in 3 phases with graudela increasing image sizes - 224, 352, 448

Set up for all CV experimetns:

IMG SIZE 224

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              128
SZ:              224
VALID:           StratifiedKFold split of combined data

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(10, 1e-2/7,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF
                 fit_one_cycle(5,  1e-2/7/5, wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_740-CV_0	0.307050	0.342956	0.757189	NB_EXP_740_CV_0_UNFREEZE_P2
EXP_740-CV_1	0.302008	0.337840	0.756192	NB_EXP_740_CV_1_UNFREEZE_P2
EXP_740-CV_2	0.318744	0.368400	0.740174	NB_EXP_740_CV_2_UNFREEZE_P2
EXP_740-CV_3	0.306437	0.330385	0.772315	NB_EXP_740_CV_3_UNFREEZE_P2
EXP_740-CV_4	0.308271	0.344059	0.758115	NB_EXP_740_CV_4_UNFREEZE_P2

IMG SIZE 352

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              352
SZ:              52
VALID:           StratifiedKFold split of combined data

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(5, 1e-3/8,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_740-CV_0	0.234084	0.252582	0.821898	NB_EXP_740_CV_0_352_UNFREEZE_P1
EXP_740-CV_1	0.243329	0.245724	0.825637	NB_EXP_740_CV_1_352_UNFREEZE_P1
EXP_740-CV_2	0.249529	0.262216	0.814094	NB_EXP_740_CV_2_352_UNFREEZE_P1
EXP_740-CV_3	0.253743	0.242828	0.832840	NB_EXP_740_CV_3_352_UNFREEZE_P1
EXP_740-CV_4	0.252761	0.249456	0.823078	NB_EXP_740_CV_4_352_UNFREEZE_P1

IMG SIZE 448

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              448
SZ:              32
VALID:           StratifiedKFold split of combined data

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(5, 1e-3/4,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_740-CV_0	0.242496	0.246118	0.815219	NB_EXP_740_CV_0_448_UNFREEZE_P1
EXP_740-CV_1	0.231280	0.231828	0.822380	NB_EXP_740_CV_1_448_UNFREEZE_P1
EXP_740-CV_2	0.248360	0.249534	0.813643	NB_EXP_740_CV_2_448_UNFREEZE_P1
EXP_740-CV_3	0.265476	0.231223	0.829990	NB_EXP_740_CV_3_448_UNFREEZE_P1
EXP_740-CV_4	0.238991	0.235502	0.826658	NB_EXP_740_CV_4_448_UNFREEZE_P1

CV SCORE:        0.821578 
LB SCORE:        0.818
SUBMISSION FLN:  EXP_740(version 32/32)

IMG SIZE 448

MODEL:           EfficientNet-B5
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
SZ:              456
BS:              32
VALID:           StratifiedKFold split of combined data

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 max_zoom(1.3),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(5, 1e-3/4,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_740-CV_0	0.232561	0.247312	0.813309	NB_EXP_740_CV_0_448_UNFREEZE_P1_rs1
EXP_740-CV_1	0.212561	0.240302	0.816792	NB_EXP_740_CV_1_448_UNFREEZE_P1_rs1
EXP_740-CV_2	0.219508	0.258105	0.805388	NB_EXP_740_CV_2_448_UNFREEZE_P1_rs1
EXP_740-CV_3	0.251555	0.237465	0.822387	NB_EXP_740_CV_3_448_UNFREEZE_P1_rs1
EXP_740-CV_4	0.235883	0.242834	0.819575	NB_EXP_740_CV_4_448_UNFREEZE_P1_rs1

CV SCORE:        0.823
LB SCORE:        0.821
SUBMISSION FLN:  XP_740_448_rs(version 53/53)

EXP_765 (LB: 0.816: PB: 0.927)

Exactly like EXP_740, except training is done in 2 phases with graudela increasing image sizes - 224, 380 with the model EfficientNet-B5. I have used Lookahead with Radam as optimizers. See Notebooks for more details

Set up for all CV experimetns:

IMG SIZE 224

MODEL:           EfficientNet-B4
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              128
SZ:              224
VALID:           StratifiedKFold split of combined data

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.01, 1.45), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(20, 1e-3,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_740-CV_0	0.284535	0.349184	0.754262	NB_EXP_765_CV_0_UNFREEZE_P1
EXP_740-CV_1	0.276863	0.350727	0.757114	NB_EXP_765_CV_1_UNFREEZE_P1
EXP_740-CV_2	0.279066	0.361914	0.745415	NB_EXP_765_CV_2_UNFREEZE_P1
EXP_740-CV_3	0.271024	0.332245	0.777068	NB_EXP_765_CV_3_UNFREEZE_P1
EXP_740-CV_4	0.282613	0.351242	0.756870	NB_EXP_765_CV_4_UNFREEZE_P1

IMG SIZE 380

MODEL:           EfficientNet-B4
NUM_CLASSES:     1 (5 classes but I am treatign this as a regression problem)
BS:              380
SZ:              64
VALID:           StratifiedKFold split of combined data

TFMS:            [flip(p=0.5), 
                 flip_vert(True), 
                 max_rotate(360), 
                 max_lighting(0.1),
                 p_lighting(0.5), 
                 zoom_crop(scale=(1.01, 1.35), do_rand=True))]
                 
NORMALIZE:       IMAGENET
TRAINING:        fit_one_cycle(5, 1e-3,   wd=1e-2, div_factor=25, pct_start=0.3)-UNF

Notebook Name	Train Loss	Valid Loss	Quadratic Kappa	Weights
EXP_740-CV_0	0.209359	0.250411	0.823727	NB_EXP_765_CV_0_380_UNFREEZE_P1
EXP_740-CV_1	0.219378	0.244317	0.827123	NB_EXP_765_CV_1_380_UNFREEZE_P1
EXP_740-CV_2	0.213898	0.256796	0.822892	NB_EXP_765_CV_2_380_UNFREEZE_P1
EXP_740-CV_3	0.213808	0.235958	0.841296	NB_EXP_765_CV_3_380_UNFREEZE_P1
EXP_740-CV_4	0.228376	0.240436	0.837558	NB_EXP_765_CV_4_380_UNFREEZE_P1

CV SCORE:        0.831
LB SCORE:        0.816
SUBMISSION FLN:  EXP_740(version 32/32)

Name		Name	Last commit message	Last commit date
Latest commit History 66 Commits
EXP_725		EXP_725
EXP_725_352		EXP_725_352
EXP_730_352_BEN		EXP_730_352_BEN
EXP_730_BEN		EXP_730_BEN
EXP_735		EXP_735
EXP_740		EXP_740
EXP_765		EXP_765
.gitignore		.gitignore
README.md		README.md

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SOLUTION WRITE UP -

Kaggle-APTOS-2019-Blindness-Detection

SETUP

EXP_725 (LB: 0.808)

EXP_725.ipynb

[EXP_725-CV_0 - EXP_725-CV_4].ipynb

EXP_725_352 (LB: 0.785)

EXP_725_352.ipynb

[EXP_725_352-CV_0 - EXP_725_352-CV_4].ipynb

EXP_730_BEN (LB: 0.804)

EXP_730.ipynb

[EXP_730-CV_0 - EXP_730-CV_4].ipynb

EXP_730_352_BEN (LB: TBD)

EXP_730_352.ipynb

EXP_735 (LB: 0.793)

EXP_735.ipynb

[EXP_735-CV_0 - EXP_735-CV_4].ipynb

EXP_740 (LB: 0.821, PB: 0.926)

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IMG SIZE 352

IMG SIZE 448

IMG SIZE 448

EXP_765 (LB: 0.816: PB: 0.927)

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