| Name | eazyml-xai-image JSON |
| Version |
0.0.24
JSON |
| download |
| home_page | https://eazyml.com/ |
| Summary | Python client for Image Explainable AI |
| upload_time | 2025-01-29 15:44:15 |
| maintainer | None |
| docs_url | None |
| author | Eazyml |
| requires_python | >=3.7 |
| license | None |
| keywords |
python
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
## Eazyml Image Explainable AI
  
This package focuses on segmentation prediction, explainability, active learning and online learning for image dataset.
### Features
- Active learning focuses on reducing the amount of labeled data required to train the model while maximizing performance, making it particularly useful when labeling data is expensive or time-consuming. By prioritizing uncertain or diverse examples, active learning accelerates model improvement and enhances efficiency.
- Online learning is a machine learning approach where models are trained incrementally as data becomes available, rather than using a fixed, pre-existing dataset. This method is well-suited for dynamic environments, enabling real-time updates and adaptability to new patterns or changes in data streams.
## Installation
### User installation
The easiest way to install augmented intelligence is using pip:
```bash
pip install -U eazyml-xai-image
```
### Dependencies
Eazyml Augmented Intelligence requires :
- tensorflow
- segmentation-models==1.0.1
- lime
- opencv-python
- flask
- pyyaml
## Usage
It provides following apis :
1. ez_image_active_learning :
This API sorts test images based on explainability scores for the model’s predictions. If a “query count” is specified in the options, it returns the indices and corresponding scores for that number of inputs.
```python
ez_image_active_learning(
filenames=['..', '..'],
model_path='path_of_model',
predicted_filenames=['path_of_model_prediction_file_names'],
options={
"query_count": 10,
"training_data_path": "path/to/training/data.csv",
"score_strategy": "weighted-moments",
"al_strategy": "pool-based",
"xai_strategy": "gradcam",
"gradcam_layer": "layer_name",
"model_num": "1"
}
)
```
2. ez_image_model_evaluate :
This API validates a model using provided data and returns the model evaluation.
```python
ez_image_model_evaluate(
validation_data_path='path_of_new_data_for_validation',
model_path='path_of_model',
options={
"required_functions": {
"loss_fn": '...',
"metric_fns": '...',
"input_preprocess_fn": '',
"label_preprocess_fn": '',
"output_process_fn": ''
},
"batch_size": 32,
"log_file": "path/to/log/file"
})
```
3. ez_image_online_learning :
This API updates a given model using new training data and saves the updated model. The update process adapts based on the Online Learning strategy or optimizes performance on provided validation data.
```python
ez_image_online_learning(
validation_data_path='path_of_new_data_for_validation',
model_path='path_of_model',
options={
"required_functions": {
"loss_fn": '...',
"metric_fns": '...',
"input_preprocess_fn": '',
"label_preprocess_fn": '',
"output_process_fn": ''
},
"batch_size": 32,
"log_file": "path/to/log/file"
}
)
```
4. ez_xai_image_explain :
This API provides confidence scores and image explanations for model predictions. It can process a single image or multiple images, returning explanations for all predictions.
```python
ez_xai_image_explain(
filenames=['..', '..'],
model_path='path_of_model',
predicted_filenames=['path_of_model_prediction_file_names'],
options={
"training_data_path": "...",
"score_strategy": "weighted-moments",
"xai_strategy": "gradcam",
"xai_image_path": "...",
"gradcam_layer": "layer_name",
"model_num": "1",
"required_functions": {...}
}
)
```
You can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_xai_image.html).
## Useful links and similar projects
- [Documentation](https://docs.eazyml.com)
- [Homepage](https://eazyml.com)
- If you have more questions or want to discuss a specific use case please book an appointment [here](https://eazyml.com/trust-in-ai)
- Here are some other EazyML's packages :
- [eazyml](https://pypi.org/project/eazyml/): Eazyml provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
- [eazyml-dq](https://pypi.org/project/eazyml-dq/): `eazyml-dq` provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and data drift analysis.
- [eazyml-cf](https://pypi.org/project/eazyml-cf/): `eazyml-cf` provides APIs for counterfactual explanations, prescriptive analytics, and actionable insights to optimize predictive outcomes.
- [eazyml-augi](https://pypi.org/project/eazyml-augi/): `eazyml-augi` provides APIs to uncover patterns, generate insights, and discover rules from training datasets.
- [eazyml-xai](https://pypi.org/project/eazyml-xai/): `eazyml-xai` provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
- [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).
## License
This project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).
---
*Maintained by [EazyML](https://eazyml.com)*
*© 2025 EazyML. All rights reserved.*
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"description": "## Eazyml Image Explainable AI \r\n  \r\n\r\n\r\n\r\nThis package focuses on segmentation prediction, explainability, active learning and online learning for image dataset.\r\n\r\n### Features\r\n- Active learning focuses on reducing the amount of labeled data required to train the model while maximizing performance, making it particularly useful when labeling data is expensive or time-consuming. By prioritizing uncertain or diverse examples, active learning accelerates model improvement and enhances efficiency.\r\n- Online learning is a machine learning approach where models are trained incrementally as data becomes available, rather than using a fixed, pre-existing dataset. This method is well-suited for dynamic environments, enabling real-time updates and adaptability to new patterns or changes in data streams.\r\n\r\n## Installation\r\n### User installation\r\nThe easiest way to install augmented intelligence is using pip:\r\n```bash\r\npip install -U eazyml-xai-image\r\n```\r\n\r\n### Dependencies\r\nEazyml Augmented Intelligence requires :\r\n- tensorflow\r\n- segmentation-models==1.0.1\r\n- lime\r\n- opencv-python\r\n- flask\r\n- pyyaml\r\n\r\n## Usage\r\nIt provides following apis :\r\n\r\n1. ez_image_active_learning :\r\nThis API sorts test images based on explainability scores for the model\u2019s predictions. If a \u201cquery count\u201d is specified in the options, it returns the indices and corresponding scores for that number of inputs.\r\n\r\n ```python\r\n ez_image_active_learning(\r\n filenames=['..', '..'],\r\n model_path='path_of_model',\r\n predicted_filenames=['path_of_model_prediction_file_names'],\r\n options={\r\n \"query_count\": 10,\r\n \"training_data_path\": \"path/to/training/data.csv\",\r\n \"score_strategy\": \"weighted-moments\",\r\n \"al_strategy\": \"pool-based\",\r\n \"xai_strategy\": \"gradcam\",\r\n \"gradcam_layer\": \"layer_name\",\r\n \"model_num\": \"1\"\r\n }\r\n )\r\n ```\r\n\r\n2. ez_image_model_evaluate :\r\nThis API validates a model using provided data and returns the model evaluation.\r\n ```python\r\n ez_image_model_evaluate(\r\n validation_data_path='path_of_new_data_for_validation',\r\n model_path='path_of_model',\r\n options={\r\n \"required_functions\": {\r\n \"loss_fn\": '...',\r\n \"metric_fns\": '...',\r\n \"input_preprocess_fn\": '',\r\n \"label_preprocess_fn\": '',\r\n \"output_process_fn\": ''\r\n },\r\n \"batch_size\": 32,\r\n \"log_file\": \"path/to/log/file\"\r\n })\r\n ```\r\n\r\n3. ez_image_online_learning :\r\nThis API updates a given model using new training data and saves the updated model. The update process adapts based on the Online Learning strategy or optimizes performance on provided validation data.\r\n ```python\r\n ez_image_online_learning(\r\n validation_data_path='path_of_new_data_for_validation',\r\n model_path='path_of_model',\r\n options={\r\n \"required_functions\": {\r\n \"loss_fn\": '...',\r\n \"metric_fns\": '...',\r\n \"input_preprocess_fn\": '',\r\n \"label_preprocess_fn\": '',\r\n \"output_process_fn\": ''\r\n },\r\n \"batch_size\": 32,\r\n \"log_file\": \"path/to/log/file\"\r\n }\r\n )\r\n ```\r\n\r\n4. ez_xai_image_explain :\r\nThis API provides confidence scores and image explanations for model predictions. It can process a single image or multiple images, returning explanations for all predictions.\r\n ```python\r\n ez_xai_image_explain(\r\n filenames=['..', '..'],\r\n model_path='path_of_model',\r\n predicted_filenames=['path_of_model_prediction_file_names'],\r\n options={\r\n \"training_data_path\": \"...\",\r\n \"score_strategy\": \"weighted-moments\",\r\n \"xai_strategy\": \"gradcam\",\r\n \"xai_image_path\": \"...\",\r\n \"gradcam_layer\": \"layer_name\",\r\n \"model_num\": \"1\",\r\n \"required_functions\": {...}\r\n }\r\n )\r\n ```\r\nYou can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_xai_image.html).\r\n\r\n## Useful links and similar projects\r\n- [Documentation](https://docs.eazyml.com)\r\n- [Homepage](https://eazyml.com)\r\n- If you have more questions or want to discuss a specific use case please book an appointment [here](https://eazyml.com/trust-in-ai)\r\n- Here are some other EazyML's packages :\r\n\r\n - [eazyml](https://pypi.org/project/eazyml/): Eazyml provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.\r\n - [eazyml-dq](https://pypi.org/project/eazyml-dq/): `eazyml-dq` provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and data drift analysis.\r\n - [eazyml-cf](https://pypi.org/project/eazyml-cf/): `eazyml-cf` provides APIs for counterfactual explanations, prescriptive analytics, and actionable insights to optimize predictive outcomes.\r\n - [eazyml-augi](https://pypi.org/project/eazyml-augi/): `eazyml-augi` provides APIs to uncover patterns, generate insights, and discover rules from training datasets.\r\n - [eazyml-xai](https://pypi.org/project/eazyml-xai/): `eazyml-xai` provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.\r\n - [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).\r\n\r\n## License\r\nThis project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).\r\n\r\n---\r\n\r\n*Maintained by [EazyML](https://eazyml.com)* \r\n*\u00a9 2025 EazyML. All rights reserved.*\r\n\r\n\r\n\r\n",
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