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Case Studies

Below, you can see some case studies we have done for computer vision applications in various fields based on some publicly available datasets from Kaggle or recently published research papers.

You can install our Python package and run your own exmples in 5 min.
You can also use this IPython notebook to train and evaluate the results for any of the above exmples or with your own data.


Pneumonia Detection

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 82%
Number of samples used for training: 5,216

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
5 5 71%
25 25 81%
50 50 87%
100 100 85%

Summary:
Best accuracy achieved with much fewer samples, 50 samples vs. 5000.
Significantly higher accuracy achieved (85% vs. 82%) with 200 training samples.

Pneumonia

Positive 1 Positive 2

Normal

Negative 1 Negative 2

Plant Bacterial Disease Detection

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 96%
Number of samples used for training: 2,475

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 86%
5 5 86%
15 15 94%
25 25 100%

Summary:
Higher accuracy achieved (100% vs. 96%) with significantly lower number of training samples, 50 samples vs. ~2500 samples.

Bacterial Disease

Positive 1 Positive 2

Healthy

Negative 1 Negative 2

Malaria Infection Detection

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 96%
Number of samples used for training: 22,049

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 88%
5 5 86%
15 15 88%
25 25 96%
50 50 96%

Summary:
Same accuracy achieved with significantly lower number of training samples, 50 samples vs. ~22,000 samples!

Parasitized

Positive 1 Positive 2

Uninfected

Negative 1 Negative 2

Fraud Detection

Dataset from Kaggle.

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 86%
5 5 88%
15 15 94%
25 25 92%
50 50 96%

Altered

Positive 1 Positive 2

Real

Negative 1 Negative 2

Fruit Classification

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 98%
Number of samples used for training: 10,900

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 81%
5 5 85%
15 15 98%
25 25 98%
50 50 99%

Summary:
Same accuracy achieved using much fewer samples (30 vs 10,900.)
Good accuracy achieved using only 10 samples.

Rotten

Positive 1 Positive 2 Positive 3

Fresh

Negative 1 Negative 2 Negative 3

COVID Detection

Dataset from Github.

The best result achieved by UC-Berkeley researchers publishe here:
Accuracy: 89%
Number of samples used for training: 700

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
10 10 65%
50 50 76%
100 100 83%
350 350 90%

Summary:
Slightly better accuracy achieved using same number of samples.
Good accuracy achieved using 200 samples.

COVID

Positive 1 Positive 2

No COVID

Negative 1 Negative 2

Brain Tumor Detection

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 88%
Number of samples used for training: 193

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
5 5 76%
15 15 86%
25 25 87%
50 50 90%

Summary:
Best accuracy achieved with fewer samples, ~50 samples vs. 193.
Higher accuracy achieved (90% vs. 88%) with 100 training samples.

Tumor

Positive 1 Positive 2

Normal

Negative 1 Negative 2

Concrete Crack Detection

You can download the dataset from here.

The best result achieved by theresearchers publishe here:
F1 Score: 90%
Number of samples used for training: 400

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 99%
5 5 99%
15 15 100%
25 25 100%

Summary:
99% accuracy achieved using only 6 samples. 100% accuracy achieved using only 30 samples.

Cracked

Positive 1 Positive 2

Not Cracked

Negative 1 Negative 2

Garbage Classification

Dataset from Kaggle.
Multi-class; number of classes: 6

Best result achieved on Kaggle as reported here:
Accuracy: 95%
Number of samples used for training: 400/class

VEDX accuracy for different number of samples used for training:

n_samples Accuracy
10/class 80%
25/class 87%
50/class 89%
100/class 90%
400/class 96%

Summary:
Slightly better accuracy achieved using same number of samples.
Good accuracy achieved using only 10/class samples.

Cardboard

Cardboard

Glass

Glass

Paper

Paper

Metal

Metal

Plastic

Plastic

Trash

Trash

Land Scene Recognition

Dataset from Kaggle.
Multi-class; number of classes: 21

Best result achieved on Kaggle as reported here:
Accuracy: 97%
Number of samples used for training: 500/class

VEDX accuracy for different number of samples used for training:

n_samples Accuracy
10/class 84%
25/class 90%
50/class 93%
100/class 95%
500/class 97%

Summary:
Best accuracy achieved using the same number of samples. Good accuracy achieved with only 10/class samples.

Agricultural Land

Agricultural

Freeway

Freeway

Beach

Beach

Dense Residential

Dense Residential

River

River

Golf Course

Golf Course

Medium Residential

Medium Residential

Airport

Airport

Harbor

Harbor

Indoor Scene Recognition

Dataset from MIT.
Multi-class; number of classes: 67

Best result they achieved as reported in their paper.:
Accuracy: 73%
Number of samples used for training: 400/class
* Note that the paper was published before the rise of Deep Learning.

VEDX accuracy for different number of samples used for training:

n_samples Accuracy
10/class 85%
25/class 90%
50/class 91%
100/class 92%
400/class 94%

Summary:
Significantly higher accuracy achieved (85% vs 73%) with much fewer samples, 10/class vs. 400/class.

Subway

Subway

Bus

Bus

Shoe Store

Shoe Store

Auditorium

Auditorium

Theater

Theater

Library

Library

Lobby

Lobby

Corridor

Corridor

Pantry

Pantry