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feat: Benchmark of crnn_mobilenet_v3 small (#523)
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* feat: add pytorch ckpts for crnn & mobilenet_v3_large

* feat: add crnn_mobilenet tf bench
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charlesmindee authored Oct 4, 2021
1 parent 51eeeaa commit e487785
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42 changes: 23 additions & 19 deletions docs/source/using_models.rst
Original file line number Diff line number Diff line change
Expand Up @@ -100,8 +100,8 @@ For a comprehensive comparison, we have compiled a detailed benchmark on publicl
* - crnn_mobilenet_v3_small
- (32, 128, 3)
- 2.1M
-
-
- 86.21
- 90.56
-
* - crnn_mobilenet_v3_large
- (32, 128, 3)
Expand Down Expand Up @@ -171,6 +171,8 @@ For a comprehensive comparison, we have compiled a detailed benchmark on publicl
+----------------------------------------+------------+---------------+---------+------------+---------------+---------+
| db_resnet50 + sar_resnet31 | 71.25 | 76.29 | 0.27 | 84.50 | **81.96** | 0.83 |
+----------------------------------------+------------+---------------+---------+------------+---------------+---------+
| db_resnet50 + crnn_mobilenet_v3_small | 69.85 | 74.80 | | 80.85 | 78.42 | 0.83 |
+----------------------------------------+------------+---------------+---------+------------+---------------+---------+
| db_mobilenet_v3_large + crnn_vgg16_bn | 67.73 | 71.73 | | 71.65 | 59.03 | |
+----------------------------------------+------------+---------------+---------+------------+---------------+---------+
| Gvision text detection | 59.50 | 62.50 | | 75.30 | 70.00 | |
Expand All @@ -190,23 +192,25 @@ FPS (Frames per second) is computed after a warmup phase of 100 tensors (where t
Since you may be looking for specific use cases, we also performed this benchmark on private datasets with various document types below. Unfortunately, we are not able to share those at the moment since they contain sensitive information.


+----------------------------------------------+----------------------------+----------------------------+----------------------------+----------------------------+
| | Receipts | Invoices | IDs | US Tax Forms |
+==============================================+============+===============+============+===============+============+===============+============+===============+
| **Architecture** | **Recall** | **Precision** | **Recall** | **Precision** | **Recall** | **Precision** | **Recall** | **Precision** |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + crnn_vgg16_bn (ours) | 78.70 | 81.12 | 65.80 | 70.70 | 50.25 | 51.78 | 79.08 | 92.83 |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + master (ours) | **79.00** | **81.42** | 65.57 | 69.86 | 51.34 | 52.90 | 78.86 | 92.57 |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + sar_resnet31 (ours) | 78.94 | 81.37 | 65.89 | **70.79** | **51.78** | **53.35** | 79.04 | 92.78 |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_mobilenet_v3_large + crnn_vgg16_bn (ours) | 78.36 | 74.93 | 63.04 | 68.41 | 39.36 | 41.75 | 72.14 | 89.97 |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| Gvision doc. text detection | 68.91 | 59.89 | 63.20 | 52.85 | 43.70 | 29.21 | 69.79 | 65.68 |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| AWS textract | 75.77 | 77.70 | **70.47** | 69.13 | 46.39 | 43.32 | **84.31** | **98.11** |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
+----------------------------------------------+----------------------------+----------------------------+----------------------------+----------------------------+----------------------------+----------------------------+
| | Receipts | Invoices | IDs | US Tax Forms | Resumes | Road Fines |
+==============================================+============+===============+============+===============+============+===============+============+===============+============+===============+============+===============+
| **Architecture** | **Recall** | **Precision** | **Recall** | **Precision** | **Recall** | **Precision** | **Recall** | **Precision** | **Recall** | **Precision** | **Recall** | **Precision** |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + crnn_vgg16_bn (ours) | 78.70 | 81.12 | 65.80 | 70.70 | 50.25 | 51.78 | 79.08 | 92.83 | | | | |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + master (ours) | **79.00** | **81.42** | 65.57 | 69.86 | 51.34 | 52.90 | 78.86 | 92.57 | | | | |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + sar_resnet31 (ours) | 78.94 | 81.37 | 65.89 | **70.79** | **51.78** | **53.35** | 79.04 | 92.78 | | | | |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_resnet50 + crnn_mobilenet_v3_small (ours) | 76.81 | 79.15 | 64.89 | 69.61 | 45.03 | 46.38 | 78.96 | 92.11 | 85.91 | 87.20 | 84.85 | 85.86 |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| db_mobilenet_v3_large + crnn_vgg16_bn (ours) | 78.36 | 74.93 | 63.04 | 68.41 | 39.36 | 41.75 | 72.14 | 89.97 | | | | |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| Gvision doc. text detection | 68.91 | 59.89 | 63.20 | 52.85 | 43.70 | 29.21 | 69.79 | 65.68 | | | | |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+
| AWS textract | 75.77 | 77.70 | **70.47** | 69.13 | 46.39 | 43.32 | **84.31** | **98.11** | | | | |
+----------------------------------------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+------------+---------------+


Two-stage approaches
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