update

README.md

@@ -18,9 +18,6 @@ tags:
 | PP-DocLayout_plus-L | pp-docLayout_plus-l.onnx | layout detection |
 | PP-OCRv6_medium_det | pp-ocrV6_medium_det.onnx | text line detection |
 | PP-OCRv6_medium_rec | pp-ocrV6_medium_rec.onnx | text recognition |
-| PP-LCNet_x1_0_table_cls | pp-lcNet_x1_0_table_cls.onnx | table classification |
-| RT-DETR-L_wired_table_cell_det | rt-detr-l_wired_table_cell_det.onnx | table cell detection (wired) |
-| RT-DETR-L_wireless_table_cell_det | rt-detr-l_wireless_table_cell_det.onnx | table cell detection (wireless) |
 
 ## Usage
 
@@ -41,7 +38,6 @@ Test images used by the examples:
 
 - [jp_1.jpg](https://github.com/cimo/One_workspace/blob/main/file/test/jp_1.jpg) = full page
 - [det_box_0294.jpg](https://github.com/cimo/One_workspace/blob/main/file/test/det_box_0294.jpg) = single text line
-- [Table_1.jpg](https://github.com/cimo/One_workspace/blob/main/file/test/Table_1.jpg) = single table
 
 ## PP-DocLayout_plus-L
 
@@ -117,73 +113,3 @@ then the blank at index 0.
 - Character map = index 0 is the blank, 1..18708 are the lines of<br>
 [PP-OCRv6_medium_rec/onnx/dictionary.txt](PP-OCRv6_medium_rec/onnx/dictionary.txt), 18709 is the space.
 - Score = mean probability of the kept time steps.
-
-## PP-LCNet_x1_0_table_cls
-
-- [PP-LCNet_x1_0_table_cls/src/example.py](PP-LCNet_x1_0_table_cls/src/example.py)
-
-```
-python3 PP-LCNet_x1_0_table_cls/src/example.py Table_1.jpg
-```
-
-```
-0.843585 | wired
-```
-
-Note:
-- Input = a single cropped table, not a full page: use the `table` boxes from PP-DocLayout_plus-L.
-- Input = RGB image with the short side resized to 256, center cropped to 224x224, scaled to 0..1<br>
-then normalized with the ImageNet mean / std, CHW with batch dimension.
-- Feed = `x`, the only input, fixed at 224x224 with a dynamic batch.
-- Output = `[1, 2]` already softmaxed, index 0 = `wired`, index 1 = `wireless`.
-- The label picks the cell detection model: `wired` = RT-DETR-L_wired_table_cell_det,<br>
-`wireless` = RT-DETR-L_wireless_table_cell_det.
-
-## RT-DETR-L_wired_table_cell_det
-
-- [RT-DETR-L_wired_table_cell_det/src/example.py](RT-DETR-L_wired_table_cell_det/src/example.py)
-
-```
-python3 RT-DETR-L_wired_table_cell_det/src/example.py Table_1.jpg
-```
-
-```
-0.953868 | [467, 189, 1012, 216]
-0.953357 | [467, 162, 1012, 189]
-0.953275 | [467, 216, 1012, 243]
-0.951546 | [467, 243, 1012, 270]
-0.950149 | [467, 135, 1012, 162]
-```
-
-Note:
-- Input = a single cropped table with the borders drawn: use it when PP-LCNet_x1_0_table_cls<br>
-answers `wired`.
-- Input = RGB image resized to 640x640, float32 0..1, CHW with batch dimension.
-- Feed = `image`, `im_shape` (640, 640), `scale_factor` (640 / height, 640 / width).
-- Output row = `[classId, score, x1, y1, x2, y2]` with coordinates already in the cropped table space,<br>
-the box count is in the second output.
-- 1 label class only, `classId` is always 0 (cell), so the class is not printed.
-- Output = always 300 rows: filter by score (0.3 in the example) and remove the duplicates on your side,<br>
-the example drops a box over 0.5 of intersection over union or over 0.9 of containment.
-- The boxes are the single cells: cluster their edges to rebuild the rows / columns and the spans.
-
-## RT-DETR-L_wireless_table_cell_det
-
-- [RT-DETR-L_wireless_table_cell_det/src/example.py](RT-DETR-L_wireless_table_cell_det/src/example.py)
-
-```
-python3 RT-DETR-L_wireless_table_cell_det/src/example.py Table_1.jpg
-```
-
-```
-0.951309 | [464, 162, 1009, 189]
-0.950602 | [464, 189, 1010, 216]
-0.950491 | [464, 216, 1010, 243]
-0.950054 | [464, 243, 1010, 270]
-0.949992 | [464, 297, 1010, 324]
-```
-
-Note:
-- Same input, feed and output of RT-DETR-L_wired_table_cell_det, only the weights change: use it when<br>
-PP-LCNet_x1_0_table_cls answers `wireless`.
-- Input = a single cropped table without the borders drawn, the cells are separated by the spacing only.