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训练集只用IC15,默认代码里的300000次step; loss普遍能下降几个数量级,但是结果不太好,使用IC15的测试集,计算hmean结果为: {'precision': 0.023610427939006393, 'recall': 0.023110255175734232, 'hmean': 0.023357664233576644, 'AP': 0}
可以使用其他repo里的hmean计算脚本,注意修改bbox的数据代码 在./nets/utils.py中的unmold_detections函数中。将bbox的代码改为:
’‘’ points[:,[0,1]] = points[:,[1,0]] rect = cv2.minAreaRect(points) # (x1, y1, x2, y2, x3, y3, x4, y4) boundbox = cv2.boxPoints(rect) bound_boxes.append([boundbox[1], boundbox[2], boundbox[3], boundbox[0]]) full_masks.append(full_mask) full_masks = np.stack(full_masks, axis=-1) if full_masks else np.empty(original_image_shape[:2] + (0,)) bound_boxes = np.array(bound_boxes) if bound_boxes else np.empty((0,8)) ‘’‘
运行推理的时候记得删除gts 具体的计算脚本可以找其他repo中的cal_recall_precison_f1
The text was updated successfully, but these errors were encountered:
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训练集只用IC15,默认代码里的300000次step;
loss普遍能下降几个数量级,但是结果不太好,使用IC15的测试集,计算hmean结果为:
{'precision': 0.023610427939006393, 'recall': 0.023110255175734232, 'hmean': 0.023357664233576644, 'AP': 0}
可以使用其他repo里的hmean计算脚本,注意修改bbox的数据代码
在./nets/utils.py中的unmold_detections函数中。将bbox的代码改为:
’‘’
points[:,[0,1]] = points[:,[1,0]]
rect = cv2.minAreaRect(points)
# (x1, y1, x2, y2, x3, y3, x4, y4)
boundbox = cv2.boxPoints(rect)
bound_boxes.append([boundbox[1], boundbox[2], boundbox[3], boundbox[0]])
full_masks.append(full_mask)
full_masks = np.stack(full_masks, axis=-1)
if full_masks else np.empty(original_image_shape[:2] + (0,))
bound_boxes = np.array(bound_boxes)
if bound_boxes else np.empty((0,8))
‘’‘
运行推理的时候记得删除gts
具体的计算脚本可以找其他repo中的cal_recall_precison_f1
The text was updated successfully, but these errors were encountered: