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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,69 +1,64 @@ | ||
| # Seeed Studio EdgeLab | ||
|
|
||
| <div align="center"> | ||
| <img width="100%" src="docs/public/images/EdgeLab-Logo.png"> | ||
| <h3> <a href="https://seeed-studio.github.io/EdgeLab/"> Documentation </a> | <a href="https://github.com/Seeed-Studio/edgelab-model-zoo"> Model Zoo </a> </h3> | ||
| </div> | ||
|
|
||
| English | [简体中文](README_zh-CN.md) | ||
|
|
||
|
|
||
| ## Introduction | ||
|
|
||
| Seeed Studio EdgeLab is an open-source project focused on embedded AI. We have optimized excellent algorithms from [OpenMMLab](https://github.com/open-mmlab) for real-world scenarios and made implemention more user-friendly, achieving faster and more accurate inference on embedded devices. | ||
|
|
||
|
|
||
| ## What's included | ||
|
|
||
| Currently we support the following directions of algorithms: | ||
|
|
||
| <details> | ||
| <summary>Anomaly Detection (coming soon)</summary> | ||
| In the real world, anomalous data is often difficult to identify, and even if it can be identified, it requires a very high cost. The anomaly detection algorithm collects normal data in a low-cost way, and anything outside normal data is considered anomalous. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Computer Vision</summary> | ||
| Here we provide a number of computer vision algorithms such as object detection, image classification, image segmentation and pose estimation. However, these algorithms cannot run on low-cost hardware. EdgeLab optimizes these computer vision algorithms to achieve good running speed and accuracy in low-end devices. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Scenario Specific</summary> | ||
| EdgeLab provides customized scenarios for specific production environments, such as identification of analog instruments, traditional digital meters, and audio classification. | ||
| </details> | ||
|
|
||
| <br> | ||
|
|
||
| We will keep adding more algorithms in the future. Stay tuned! | ||
|
|
||
|
|
||
| ## Features | ||
|
|
||
| <details> | ||
| <summary>User-friendly</summary> | ||
| EdgeLab provides a user-friendly platform that allows users to easily perform training on collected data, and to better understand the performance of algorithms through visualizations generated during the training process. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Models with low computing power and high performance</summary> | ||
| EdgeLab focuses on end-side AI algorithm research, and the algorithm models can be deployed on microprocessors, similar to <a href="https://www.espressif.com/en/products/socs/esp32">ESP32</a>, some <a href="https://arduino.cc">Arduino</a> development boards, and even in embedded SBCs such as <a href="https://www.raspberrypi.org">Raspberry Pi</a>. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Supports mutiple formats for model export</summary> | ||
| <a href="https://www.tensorflow.org/lite">TensorFlow Lite</a> is mainly used in microcontrollers, while <a href="https://onnx.ai">ONNX</a> is mainly used in devices with Embedded Linux. There are some special formats such as <a href="https://developer.nvidia.com/tensorrt">TensorRT</a>, <a href="https://docs.openvino.ai">OpenVINO</a> which are already well supported by OpenMMlab. EdgeLab has added TFLite model export for microcontrollers, which can be directly converted to uf2 format and drag-and-drop into the device for deployment. | ||
| </details> | ||
|
|
||
|
|
||
| ## Acknowledgement | ||
|
|
||
| EdgeLab referenced the following projects: | ||
|
|
||
| - [OpenMMLab](https://openmmlab.com/) | ||
| - [ONNX](https://github.com/onnx/onnx) | ||
| - [NCNN](https://github.com/Tencent/ncnn) | ||
|
|
||
|
|
||
| ## License | ||
|
|
||
| This project is released under the [MIT license](LICENSES). | ||
| # Seeed Studio EdgeLab | ||
|
|
||
| <div align="center"> | ||
| <img width="100%" src="docs/public/images/EdgeLab-Logo.png"> | ||
| <h3> <a href="https://seeed-studio.github.io/EdgeLab/"> Documentation </a> | <a href="https://github.com/Seeed-Studio/edgelab-model-zoo"> Model Zoo </a> </h3> | ||
| </div> | ||
|
|
||
| English | [简体中文](README_zh-CN.md) | ||
|
|
||
| ## Introduction | ||
|
|
||
| Seeed Studio EdgeLab is an open-source project focused on embedded AI. We have optimized excellent algorithms from [OpenMMLab](https://github.com/open-mmlab) for real-world scenarios and made implementation more user-friendly, achieving faster and more accurate inference on embedded devices. | ||
|
|
||
| ## What's included | ||
|
|
||
| Currently we support the following directions of algorithms: | ||
|
|
||
| <details> | ||
| <summary>Anomaly Detection (coming soon)</summary> | ||
| In the real world, anomalous data is often difficult to identify, and even if it can be identified, it requires a very high cost. The anomaly detection algorithm collects normal data in a low-cost way, and anything outside normal data is considered anomalous. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Computer Vision</summary> | ||
| Here we provide a number of computer vision algorithms such as object detection, image classification, image segmentation and pose estimation. However, these algorithms cannot run on low-cost hardware. EdgeLab optimizes these computer vision algorithms to achieve good running speed and accuracy in low-end devices. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Scenario Specific</summary> | ||
| EdgeLab provides customized scenarios for specific production environments, such as identification of analog instruments, traditional digital meters, and audio classification. | ||
| </details> | ||
|
|
||
| <br> | ||
|
|
||
| We will keep adding more algorithms in the future. Stay tuned! | ||
|
|
||
| ## Features | ||
|
|
||
| <details> | ||
| <summary>User-friendly</summary> | ||
| EdgeLab provides a user-friendly platform that allows users to easily perform training on collected data, and to better understand the performance of algorithms through visualizations generated during the training process. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Models with low computing power and high performance</summary> | ||
| EdgeLab focuses on end-side AI algorithm research, and the algorithm models can be deployed on microprocessors, similar to <a href="https://www.espressif.com/en/products/socs/esp32">ESP32</a>, some <a href="https://arduino.cc">Arduino</a> development boards, and even in embedded SBCs such as <a href="https://www.raspberrypi.org">Raspberry Pi</a>. | ||
| </details> | ||
|
|
||
| <details> | ||
| <summary>Supports multiple formats for model export</summary> | ||
| <a href="https://www.tensorflow.org/lite">TensorFlow Lite</a> is mainly used in microcontrollers, while <a href="https://onnx.ai">ONNX</a> is mainly used in devices with Embedded Linux. There are some special formats such as <a href="https://developer.nvidia.com/tensorrt">TensorRT</a>, <a href="https://docs.openvino.ai">OpenVINO</a> which are already well supported by OpenMMlab. EdgeLab has added TFLite model export for microcontrollers, which can be directly converted to uf2 format and drag-and-drop into the device for deployment. | ||
| </details> | ||
|
|
||
| ## Acknowledgement | ||
|
|
||
| EdgeLab referenced the following projects: | ||
|
|
||
| - [OpenMMLab](https://openmmlab.com/) | ||
| - [ONNX](https://github.com/onnx/onnx) | ||
| - [NCNN](https://github.com/Tencent/ncnn) | ||
|
|
||
| ## License | ||
|
|
||
| This project is released under the [MIT license](LICENSES). |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,85 +1,84 @@ | ||
| # dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
|
|
||
| # file_client_args = dict( | ||
| # backend='petrel', | ||
| # path_mapping=dict({ | ||
| # './data/': 's3://openmmlab/datasets/detection/', | ||
| # 'data/': 's3://openmmlab/datasets/detection/' | ||
| # })) | ||
| file_client_args = dict(backend='disk') | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='PackDetInputs') | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | ||
| # If you don't have a gt annotation, delete the pipeline | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor')) | ||
| ] | ||
| train_dataloader = dict( | ||
| batch_size=2, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| batch_sampler=dict(type='AspectRatioBatchSampler'), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='annotations/instances_train2017.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | ||
| pipeline=train_pipeline)) | ||
| val_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='annotations/instances_val2017.json', | ||
| data_prefix=dict(img='val2017/'), | ||
| test_mode=True, | ||
| pipeline=test_pipeline)) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict( | ||
| type='CocoMetric', | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| metric='bbox', | ||
| format_only=False) | ||
| test_evaluator = val_evaluator | ||
|
|
||
| # inference on test dataset and | ||
| # format the output results for submission. | ||
| # test_dataloader = dict( | ||
| # batch_size=1, | ||
| # num_workers=2, | ||
| # persistent_workers=True, | ||
| # drop_last=False, | ||
| # sampler=dict(type='DefaultSampler', shuffle=False), | ||
| # dataset=dict( | ||
| # type=dataset_type, | ||
| # data_root=data_root, | ||
| # ann_file=data_root + 'annotations/image_info_test-dev2017.json', | ||
| # data_prefix=dict(img='test2017/'), | ||
| # test_mode=True, | ||
| # pipeline=test_pipeline)) | ||
| # test_evaluator = dict( | ||
| # type='CocoMetric', | ||
| # metric='bbox', | ||
| # format_only=True, | ||
| # ann_file=data_root + 'annotations/image_info_test-dev2017.json', | ||
| # outfile_prefix='./work_dirs/coco_detection/test') | ||
| # dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
|
|
||
| # file_client_args = dict( | ||
| # backend='petrel', | ||
| # path_mapping=dict({ | ||
| # './data/': 's3://openmmlab/datasets/detection/', | ||
| # 'data/': 's3://openmmlab/datasets/detection/' | ||
| # })) | ||
| file_client_args = dict(backend='disk') | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='PackDetInputs'), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | ||
| # If you don't have a gt annotation, delete the pipeline | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')), | ||
| ] | ||
| train_dataloader = dict( | ||
| batch_size=2, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| batch_sampler=dict(type='AspectRatioBatchSampler'), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='annotations/instances_train2017.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | ||
| pipeline=train_pipeline, | ||
| ), | ||
| ) | ||
| val_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='annotations/instances_val2017.json', | ||
| data_prefix=dict(img='val2017/'), | ||
| test_mode=True, | ||
| pipeline=test_pipeline, | ||
| ), | ||
| ) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict( | ||
| type='CocoMetric', ann_file=data_root + 'annotations/instances_val2017.json', metric='bbox', format_only=False | ||
| ) | ||
| test_evaluator = val_evaluator | ||
|
|
||
| # inference on test dataset and | ||
| # format the output results for submission. | ||
| # test_dataloader = dict( | ||
| # batch_size=1, | ||
| # num_workers=2, | ||
| # persistent_workers=True, | ||
| # drop_last=False, | ||
| # sampler=dict(type='DefaultSampler', shuffle=False), | ||
| # dataset=dict( | ||
| # type=dataset_type, | ||
| # data_root=data_root, | ||
| # ann_file=data_root + 'annotations/image_info_test-dev2017.json', | ||
| # data_prefix=dict(img='test2017/'), | ||
| # test_mode=True, | ||
| # pipeline=test_pipeline)) | ||
| # test_evaluator = dict( | ||
| # type='CocoMetric', | ||
| # metric='bbox', | ||
| # format_only=True, | ||
| # ann_file=data_root + 'annotations/image_info_test-dev2017.json', | ||
| # outfile_prefix='./work_dirs/coco_detection/test') |
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