Spaces:
Runtime error
Runtime error
Update README.md
Browse files
README.md
CHANGED
|
@@ -14,16 +14,20 @@ Our project aims to develop an image classification system capable of distinguis
|
|
| 14 |
By leveraging a combination of classification techniques and machine learning, we aim to create a model that can accurately classify different types of images and detect the critical differences between works of art.
|
| 15 |
For this project, we utilized several models, including CNN, ELA, RESNET50, and VIT.
|
| 16 |
|
|
|
|
|
|
|
| 17 |
After building and running these models and evaluating their prediction results, this is the evaluation of Results:
|
| 18 |
|
| 19 |
-
It can be observed that, according to the *Accuracy* metric,
|
| 20 |
|
|
|
|
| 21 |
|
| 22 |
-
According to the *Recall* metric, we set a performance threshold of at least 80%, and there are two models that meet this requirement: the *CNN+ELA* model (83.5%) and the *ViT* model (95.7%).
|
| 23 |
|
| 24 |
The following table presents the test metric results for all the models implemented in this project.
|
| 25 |
|
| 26 |
<img src="https://cdn-uploads.huggingface.co/production/uploads/66d6f28a19214d743ca1eb43/q6g7SAHT-enMFOkoXqxWc.png" alt="Description" width="500" style="display: block; margin-left: auto; margin-right: auto;"/>
|
| 27 |
|
| 28 |
|
| 29 |
-
**After comparing the different results, it can be seen that the model with the highest performance across all metrics is the ***ViT*** model, achieving the best results according to all the criteria we set in the initial phase.**
|
|
|
|
|
|
|
|
|
| 14 |
By leveraging a combination of classification techniques and machine learning, we aim to create a model that can accurately classify different types of images and detect the critical differences between works of art.
|
| 15 |
For this project, we utilized several models, including CNN, ELA, RESNET50, and VIT.
|
| 16 |
|
| 17 |
+
---
|
| 18 |
+
|
| 19 |
After building and running these models and evaluating their prediction results, this is the evaluation of Results:
|
| 20 |
|
| 21 |
+
<span style="font-size:18px"> It can be observed that, according to the *Accuracy* metric, two models meet the desired threshold of at least 85%, which are: the *CNN+ELA* model (85%) and the *ViT* model (92%).</span>
|
| 22 |
|
| 23 |
+
<span style="font-size:18px">According to the *Recall* metric, we set a performance threshold of at least 80%, and there are two models that meet this requirement: the *CNN+ELA* model (83.5%) and the *ViT* model (95.7%).</span>
|
| 24 |
|
|
|
|
| 25 |
|
| 26 |
The following table presents the test metric results for all the models implemented in this project.
|
| 27 |
|
| 28 |
<img src="https://cdn-uploads.huggingface.co/production/uploads/66d6f28a19214d743ca1eb43/q6g7SAHT-enMFOkoXqxWc.png" alt="Description" width="500" style="display: block; margin-left: auto; margin-right: auto;"/>
|
| 29 |
|
| 30 |
|
| 31 |
+
**After comparing the different results, it can be seen that the model with the highest performance across all metrics is the ***ViT*** model, achieving the best results according to all the criteria we set in the initial phase.**
|
| 32 |
+
|
| 33 |
+
---
|