mindspore2022/mindspore/lite/test/config/models_tflite_fp16.cfg

# [first column]:model_name;input_num;input_shape;threads;extra_info. If there is no need to set these parameters, the
#                content after ";" can be omitted.
# [second column]:accuracy limit for float16 in arm64 device
hiai_model_0909_kd_rot_ps_softmax.tflite 10
hiai_chinese_english_recognize_model_float32.tflite 13
hiai_bigmodel_ghost_2_1_no_normalized_no_trans_tflite.tflite 10
hiai_bigmodel_ghost_5_1_no_normalized_no_trans_tflite.tflite 10
hiai_cn_recognize_modify_padv2.tflite 14
hiai_model_normalize_object_scene_ps_20200519.tflite 20
#hiai_detectmodel_06_23_960_480_1180700.tflite 20
#hiai_detect_curve_model_float32.tflite 20
#hiai_detectmodel_desnet_256_128_64_32.tflite 20
mtk_AADB_HADB_MBV2_model_fp32.tflite 2
mtk_AADB_HADB_MBV3_model_fp32.tflite 6
mobilenet_v1_0.25_128.tflite 5
mobilenet_v1_0.25_160.tflite 5
mobilenet_v1_0.25_192.tflite 3
mobilenet_v1_0.25_224.tflite 3
mobilenet_v1_0.5_128.tflite 2.5
mobilenet_v1_0.5_160.tflite 5
mobilenet_v1_0.5_192.tflite 6
mobilenet_v1_0.5_224.tflite 5
mobilenet_v1_0.75_128.tflite 6
mobilenet_v1_0.75_160.tflite 6
mobilenet_v1_0.75_192.tflite 6
mobilenet_v1_0.75_224.tflite 6
mobilenet_v1_1.0_128.tflite 10
mobilenet_v1_1.0_160.tflite 6
mobilenet_v1_1.0_192.tflite 26
mobilenet_v1_1.0_224.tflite 6
mobilenet_v2_1.0_224.tflite 3
mtk_model_normalize_object_scene_ps_20200519_f32.tflite 20
mtk_model_ckpt.tflite 20
mtk_age_gender.tflite 0.1
mtk_model_face_dress.tflite 2
mtk_face_features_v1.tflite 21
densenet.tflite 36
squeezenet.tflite 6
resnet_v2_101_299.tflite 3
mnasnet_1.3_224.tflite 12
inception_v3.tflite 3
deeplabv3_257_mv_gpu.tflite 3
multi_person_mobilenet_v1_075_float.tflite 9
#hiai_vad.tflite
ide_label_base.tflite 22
# ide_label_retrained.tflite involves a softmax-like structure whose output channel is 12.
# The values in the first few channels are extremely small and casted into 0 in the fp16 subgraph.
# The next slice operator takes out data from the first 6 channels, which are all zero, causing the error of
# dividing 0 in the following operator.
#ide_label_retrained.tflite
ml_ei_headpose.tflite 3
ml_ei_landmark.tflite 3
mnist.tflite 4
mobilenet.tflite 0.1
resnet.tflite 120
scan_hms_angle1.tflite 6
scan_hms_detect.tflite 12
hiai_latin_ocr.tflite 45
hiai_latin_ocr_1.tflite 14.5
ml_ocr_jk.tflite 2
nasnet_mobile.tflite 3
nasnet_large.tflite 3
inception_resnet_v2.tflite 10
ml_ocr_latin.tflite 15
hiai_PoseEstimation_Pcm.tflite 15
hiai_ssd_mobilenetv2_object.tflite 60
hiai_cv_focusShootOCRModel_02.tflite 13
hiai_cv_poseEstimation.tflite 190
inception_v4.tflite 10
mtk_model_normalize_object_scene_ps_20200519_f16.tflite 10
mtk_model_face_dress_fp16.tflite 45
mtk_AADB_HADB_MBV2_model_f16.tflite 5
mtk_AADB_HADB_MBV3_model_f16.tflite 10
mtk_model_emotions_0725_fp16.tflite 3
mtk_face_features_v1_fp16.tflite 20
siteAI_digcom_AI_ECN.tflite 0.1
siteAI_digcom_g2v_keras.tflite 5
siteAI_trans_nonlinear.tflite 0.2
siteAI_trans_tcpclassify.tflite 5.3
siteAI_wireless_depress_w.tflite 8
siteAI_wireless_restore_w.tflite 0.1
magenta_arbitrary-image-stylization-v1-256_fp16_prediction_1.tflite 5
ml_object_detect.tflite 3
ml_object_detect_1.tflite 5
hiai_cpu_face_emotion.tflite 6
hiai_cpu_face_gazing.tflite 5
hiai_cpu_face_headpose.tflite 100
hiai_humanDetection.tflite 15
#hiai_cv_focusShootOCRModel_08.tflite 0.3
#ml_face_openclose.tflite 40
hiai_face_model_npu.tflite 5
hiai_ctpn_feature_map.tflite 10
hiai_cv_labelDetectorModel_v2.tflite 30
hiai_cv_labelDetectorModel_v4.tflite 3
hiai_dress_detect.tflite 3
hiai_cv_saliencyDetectorModel.tflite 3
hiai_frozen_inference_graph.tflite 5
hiai_ghostnet.tflite 3
#hiai_iMaxDN_RGB.tflite 0.4
#hiai_iMaxSR_RGB.tflite 1
hiai_label_and_video.tflite 10
hiai_lm_inference_graph.tflite 3
efficientnet_lite0_fp32_2.tflite 6
efficientnet_lite1_fp32_2.tflite 6
efficientnet_lite2_fp32_2.tflite 3
efficientnet_lite3_fp32_2.tflite 6
efficientnet_lite4_fp32_2.tflite 6
mnasnet_0.50_224_1_metadata_1.tflite 5
mnasnet_0.75_224_1_metadata_1.tflite 6
mnasnet_1.0_128_1_metadata_1.tflite 6
mnasnet_1.0_160_1_metadata_1.tflite 6
mnasnet_1.0_192_1_metadata_1.tflite 8
mnasnet_1.0_224_1_metadata_1.tflite 6
mnasnet_1.0_96_1_metadata_1.tflite 6
lite-model_on_device_vision_classifier_popular_us_products_V1_1.tflite 16
lite-model_on_device_vision_classifier_popular_wine_V1_1.tflite 80
posenet_mobilenet_float_075_1_default_1.tflite 49
deeplabv3_1_default_1.tflite 6
lite-model_deeplabv3-mobilenetv2_dm05-float16_1_default_1.tflite 13
lite-model_deeplabv3-mobilenetv2-float16_1_default_1.tflite 60
lite-model_east-text-detector_fp16_1.tflite 60
lite-model_cartoongan_fp16_1.tflite 3
lite-model_arbitrary-image-stylization-inceptionv3_fp16_predict_1.tflite 6
gts_detect_5k_tf115.tflite 9.5
mtk_isface.tflite 0.2
mtk_landmark.tflite 0.3
mtk_new_detect.tflite 3
mtk_pose.tflite 2
mtk_model_emotions_0727_nosoftmax.tflite 2
mtk_model_normalize_object_scene_ps_20200826_f32_no_softmax.tflite 22
mtk_276landmark_0913.tflite 16
mtk_face_recognition.tflite 8
mtk_convert_model.tflite 5.3
smartreply.tflite 0.1
mindspore_text_classification_tflite.tflite 9.2 # small output causes big bias
#ml_location.tflite 0.1
ml_text_correction.tflite 1
# ml_pic_shopping.tflite involves subtract two close numbers.
# In fp16 case, such subtract will cause a great relative error comparing to fp32.
# e.g. fp32: 27.5 -27.4 = 0.1
#      fp16: 27.6 - 27.4 = 0.2
#ml_pic_shopping.tflite 0.1
ml_vision_guide_detection3_pb2tflite.tflite 0.5
ml_vision_guide_detection1_pb2tflite.tflite 0.5
ml_pic_shopping_pb2tflite.tflite 95
ml_ocr_jk_pb2tflite.tflite 0.5
ml_ocr_latin_pb2tflite.tflite 11.5
scan_hms_angle_pb2tflite.tflite 2.5
scan_hms_detect_pb2tflite.tflite 1.5
ml_location.tflite 0.6
ml_face_openclose_tflite.tflite 0.5
ml_object_detect_pb2tflite.tflite 1.5
# lite-model_on_device_vision_classifier_landmarks_classifier* models' bias are caused by error accumulation and small
# output value
lite-model_on_device_vision_classifier_landmarks_classifier_africa_V1_1.tflite 10
lite-model_on_device_vision_classifier_landmarks_classifier_north_america_V1_1.tflite 19
lite-model_on_device_vision_classifier_landmarks_classifier_asia_V1_1.tflite 25
lite-model_on_device_vision_classifier_landmarks_classifier_oceania_antarctica_V1_1.tflite 11
lite-model_on_device_vision_classifier_landmarks_classifier_europe_V1_1.tflite 32
lite-model_on_device_vision_classifier_landmarks_classifier_south_america_V1_1.tflite 14
ml_ei_landmark_pb2tflite.tflite 2
unet_mbv2_05_104pts.tflite 17
hiai_AADB_HADB_MBV2_model_f16.tflite 3.5
hiai_AADB_HADB_MBV2_model_fp32.tflite 4.5
mtk_age_gender_fp16.tflite 26
hiai_detect_curve_model_float32.tflite 9
Q_language_model_hrmini_Q4_b4_17w.tflite 3.5
lite-model_aiy_vision_classifier_food_V1_1.tflite 47.5
lite-model_disease-classification_1.tflite 70 
lite-model_models_mushroom-identification_v1_1.tflite 5
smartreply_1_default_1.tflite 0.5
text_classification.tflite 0.5
Q_AADB_HADB_MBV2_model.tflite 5
# the input of Q_convert model is between 0-255
Q_convert.tflite 12
# the input of Q_crnn_ori_75w_slim model is between 0-255, but its outputs has small values (e-6).
Q_crnn_ori_75w_slim_norm_pb2tflite.tflite 29
# the output of Q_crnn_ori_v2 model has small values (e-4).
Q_crnn_ori_v2_405001_notrans_nopre_pb2tflite.tflite 36
# the inputs of two Q_crnn_screen_slim400w models are between 0-255, but their outputs have small values (e-7).
Q_crnn_screen_slim400w_more_20w_pb2tflite.tflite 71
Q_dila-small-mix-full-fineturn-390000-nopixel-nosigmoid_tflite.tflite 1.5
# the inputs of two Q_focusocr models are between 0-255, but their outputs have small values (e-6).
Q_focusocr_cn_recog.tflite 30
Q_focusocr_jk_recog.tflite 25
Q_inception-249970-672-11-16_pb2tflite.tflite 6
# the input of Q_object_scene model is between 0-255
Q_object_scene.tflite 3
Q_detect_fpn_add_inception-1448650.tflite 1
bloom_landmark.tflite 0.5
# input data: 0~255
Q888_age_gender_orderd.tflite 1.5
Q888_face_dress_mv3y.tflite 0.5
Q888_HADB_AADB_MBV2_model_fp32.tflite 2.5
Q888_landmark.tflite 0.5
Q888_pose.tflite 6.1
# the output contains value less than e-7
Q888_lapa158_unet_0924.tflite 20
Q888_isface.tflite 1.0
Q888_new_detect.tflite 1.5
Q888_model_normalize_object_scene_ps_20200826_f32_no_softmax.tflite 2
# input data: -1~1
Q888_face_emo_dress_mv3_orderd.tflite 2.5
Q_iMaxDN_RGB_385_p_RGB_RGB_pb2tflite.tflite 1
Q_iMaxSR_RGB_385_p_pb2tflite.tflite 5
bloom_new_detect.tflite 3.5
bloom_model_age_gender.tflite 0.5
bloom_isface.tflite 0.5
# The output values of conv layers range from -e±5 to e±5, which almost reaches the representation limit of fp16. In
# this range, the fp16 data will has big bias. And the accumulation of this bias lowers the final precision.
hiai_object_detect_814.tflite 14
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2 12.1
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2 0.5
hdc_tb_cn_neg.tflite;3 295
# The input of hiai_cv_labelDetectorModel_v3.tflite is between 0-255.
hiai_cv_labelDetectorModel_v3.tflite;2 2
ml_headpose_pb2tflite.tflite;3;1,64,64,3:16:16 1
ml_ei_headpose_pb2tflite.tflite;3;1,64,64,3:16:16 0.6
coco_ssd_mobilenet_v1_1.0.tflite
hiai_asr_last_e1_cpu_fast_wavenet_batch1_frame1_one_cache_fp32.tflite;2
hiai_asr_ctc.tflite;2