add model commit
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@ -11,7 +11,7 @@ ml_hardware_eyeclose
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ml_ocr_detect_20200305
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Mnet6_0312_extract_pay
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pose_3d
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RFB-Epoch-170-no-transpose
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hiai_face_RFB-Epoch-170-no-transpose
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tracking
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mtk_isface
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mtk_landmark
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@ -53,8 +53,8 @@ hiai_face_recognition_1
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hiai_cpu_face_detect
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hiai_cpu_face_attr
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hiai_face_attr1
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detect-mbv1-shortcut-400-400_nopostprocess_simplified
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detect_mbv1_640_480_nopostprocess_simplified
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mtk_detect-mbv1-shortcut-400-400_nopostprocess_simplified
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mtk_detect_mbv1_640_480_nopostprocess_simplified
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retinaface
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deconv_test_model
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deconvs_model
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@ -11,14 +11,15 @@ ml_hardware_eyeclose 0.1
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ml_ocr_detect_20200305 10
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Mnet6_0312_extract_pay 15
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pose_3d 90
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RFB-Epoch-170-no-transpose 4
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hiai_face_RFB-Epoch-170-no-transpose 4
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tracking 4
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mtk_landmark 1
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mtk_pose_tuku 1
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mtk_face_recognition_v1 20
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mtk_2012_ATLANTA_10class_20190614_v41 4
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mtk_detect-deeper-halfdeeper-mbv1-lastearlySSD-shortcut-400-400_nopostprocess_simplified 4
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detect-deeper-halfdeeper-mbv1-shortcut-400-400_nopostprocess_simplified 1
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# mtk_detect-deeper-halfdeeper-mbv1-shortcut-400-400_nopostprocess_simplified: precision is 5%
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detect-deeper-halfdeeper-mbv1-shortcut-400-400_nopostprocess_simplified 5.5
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hiai_face_detect_rfb 4
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hiai_face_isface 0.1
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hiai_face_landmark 0.2
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@ -52,8 +53,9 @@ hiai_face_recognition_1 10
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hiai_cpu_face_detect 4
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hiai_cpu_face_attr 12
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hiai_face_attr1 12
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detect-mbv1-shortcut-400-400_nopostprocess_simplified 8
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detect_mbv1_640_480_nopostprocess_simplified 6
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# mtk_detect-mbv1-shortcut-400-400_nopostprocess_simplified: precision is 5%
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mtk_detect-mbv1-shortcut-400-400_nopostprocess_simplified 5.5
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mtk_detect_mbv1_640_480_nopostprocess_simplified 5
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retinaface 6
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deconv_test_model 20
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deconvs_model 1
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@ -85,6 +87,7 @@ ml_video_edit_reid 1
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ml_video_edit_v10_best_model_nomean_20200723 5
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ml_video_edit_img_segment 3
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ml_video_edit_video_segment_gauss_adaptis_part1 5
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# When the input range is [-1,1], the precision is poor, and the output value is very small (10e-5). If the input range is adjusted to [0,255], the precision will decrease to 15.5415%, and the rest is cumulative error.
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ml_handpose 175
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hdc_Face_Aesthetic_MTI_Aesthetic 22
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ml_face_compare 5.5
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@ -96,10 +99,13 @@ ml_face_isface 0.5
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ml_face_glasses 2.5
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# ml_segmentation_matting 26 # output value unstable
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ml_segmentation_atlanta_10 5
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# ml_bodymask: The difference of output node divided by a very small value leads to a large error
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ml_bodymask 14
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ml_Hand_deploy 4
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# ml_hand_3d_detection: The difference of output node divided by a very small value leads to a large error
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ml_hand_3d_detection 12
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ml_hand_3d_regression 3
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# ml_ARengine23_bodypose: The difference of output node divided by a very small value leads to a large error
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ml_ARengine23_bodypose 56
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ml_ocr_bank_card_detection_inception_tmp 20
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ml_ocr_bank_card_recognition_fcny 0.5
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@ -114,6 +120,7 @@ ml_2012_ocr_detection_caffe_tmp 1
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ml_2012_ocr_rec_caffe 0.5
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ml_lable_model_hebing_device 2
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ml_face_sex 0.5
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# ml_face_mnet: The precision problem caused by cumulative error.
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ml_face_mnet 12
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ml_segmentation_atlanta_1 0.5
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bolt_deploy_color-server 0.5
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@ -24,4 +24,5 @@ quant_aware_identify_card_detect.onnx
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tiny-yolov3-11.onnx;2;1,416,416,3:1,2
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# cur acc for ml_video_edit_art_transfer is 2+%
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ml_video_edit_art_transfer.onnx;3
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#ml_table_detection.onnx: onnx quantized model
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ml_table_detection.onnx
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@ -2,26 +2,30 @@ ml_vision_guide_detection1.pb 0.5
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ml_vision_guide_detection3.pb 0.5
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ml_video_edit_generate_filter.pb 2
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ml_ocr_jk.pb 0.5
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ml_ocr_latin.pb 135
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# The accumulated error causes the threshold to be exceeded
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ml_ocr_latin.pb 12
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scan_hms_angle.pb 1.5
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scan_hms_detect.pb 2.5
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ml_face_openclose.pb;1,32,32,3 0.5
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ml_object_detect.pb;1,288,288,3 2
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# the inputs of two Q_crnn_screen_slim400w models are between 0-255, but their outputs have small values (e-7).
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# The inputs of two Q_crnn_screen_slim400w models are between 0-255, but their outputs have small values (e-7).
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Q_crnn_screen_slim400w_more_20w.pb 72
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Q_inception-249970-672-11-16.pb 6.5
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hiai_ssd_mobilenetv2_object.pb 15
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hiai_humanDetection.pb 3.5
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hiai_PoseEstimation_Pcm.pb 0.5
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# The last layer has a very small value, which leads to a large error
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hiai_cn_recognize_modify_padv2.pb;1,32,512,1 27
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hiai_model_normalize_object_scene_ps_20200519.pb;1,224,224,3 17
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# the output of mtk_model_ckpt.pb has small value
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# The output of mtk_model_ckpt.pb has small value
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mtk_model_ckpt.pb 19
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mtk_age_gender.pb 0.5
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# The Difference of output node divided by 0 results in cumulative deviation
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mtk_model_normalize_object_scene_ps_20200519.pb;1,224,224,3 10
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# Bccumulative error of conv_batchnorm_fused op
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mtk_AADB_HADB_MBV2_model.pb;1,224,224,3 5.5
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mtk_AADB_HADB_MBV3_model.pb;1,224,224,3 4
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# the output of mtk_face_features_v1.pb has small value
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# The output of mtk_face_features_v1.pb has small value
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mtk_face_features_v1.pb 26
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model_normalize_object_scene_ps_20200519.pb;1,224,224,3 10
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hiai_AADB_HADB_MBV2_model.pb;1,224,224,3 6
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@ -39,12 +43,14 @@ hiai_cpu_face_gazing.pb 0.5
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hiai_cpu_face_emotion.pb 2
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hiai_cv_poseEstimation.pb 103
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Q_dila-small-mix-full-fineturn-390000-nopixel-nosigmoid.pb 1.5
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# the input of Q_crnn_ori_75w_slim model is between 0-255, but its outputs has small values (e-6).
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# The input of Q_crnn_ori_75w_slim model is between 0-255, but its outputs has small values (e-6).
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Q_crnn_ori_75w_slim_norm.pb 37
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# the output of Q_crnn_ori_v2 model has small values (e-4).
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# The output of Q_crnn_ori_v2 model has small values (e-4).
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Q_crnn_ori_v2_405001_notrans_nopre.pb 24
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# the input of hiai_latin models are between 0-255
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# The input of hiai_latin models are between 0-255
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hiai_latin_ocr.pb 4
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hiai_latin_ocr_1.pb 3.5
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hiai_cpu_face_headpose.pb 4
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# ml_noya_tts_melgan.pb If the input range is adjusted to [- 1,1], the fp16 error can be reduced to 38.9512%
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ml_noya_tts_melgan.pb;16,16,80 70
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bolt_segment.pb 2
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@ -31,3 +31,4 @@ add_uint8.tflite;2
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ml_Heatmap_depth_240180;2
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ml_Heatmap_depth_180240;2
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hiai_nlu_model.pb;3;1,16:1,16:1,16
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gts_object_detect_lcs.pb;1;420,630,3
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