官方模型库
领域套件与扩展包
大语言模型
model |
mindformers |
---|---|
llama2_7b, llama2_13b, llama2_7b_lora, llama2_13b_lora, llama2_70b |
|
llama3_8b |
|
glm2_6b, glm2_6b_lora |
|
glm3_6b, glm3_6b_lora |
|
gpt2, gpt2_13b |
|
baichuan2_7b, baichuan2_13b, baichuan2_7b_lora, baichuan2_13b_lora |
|
qwen_7b, qwen_14b, qwen_7b_lora, qwen_14b_lora |
|
qwen1.5-14b, qwen1.5-72b |
|
codegeex2_6b |
|
codellama_34b |
|
deepseek-coder-33b-instruct |
|
internlm_7b, internlm_20b, internlm_7b_lora |
|
mixtral-8x7b |
|
wizardcoder_15b |
|
yi_6b,yi_34b |
图像分类(骨干类)
model |
acc@1 |
mindcv |
---|---|---|
vgg11 |
71.86 |
|
vgg13 |
72.87 |
|
vgg16 |
74.61 |
|
vgg19 |
75.21 |
|
resnet18 |
70.21 |
|
resnet34 |
74.15 |
|
resnet50 |
76.69 |
|
resnet101 |
78.24 |
|
resnet152 |
78.72 |
|
resnetv2_50 |
76.90 |
|
resnetv2_101 |
78.48 |
|
dpn92 |
79.46 |
|
dpn98 |
79.94 |
|
dpn107 |
80.05 |
|
dpn131 |
80.07 |
|
densenet121 |
75.64 |
|
densenet161 |
79.09 |
|
densenet169 |
77.26 |
|
densenet201 |
78.14 |
|
seresnet18 |
71.81 |
|
seresnet34 |
75.36 |
|
seresnet50 |
78.31 |
|
seresnext26 |
77.18 |
|
seresnext50 |
78.71 |
|
skresnet18 |
73.09 |
|
skresnet34 |
76.71 |
|
skresnet50_32x4d |
79.08 |
|
resnext50_32x4d |
78.53 |
|
resnext101_32x4d |
79.83 |
|
resnext101_64x4d |
80.30 |
|
resnext152_64x4d |
80.52 |
|
rexnet_x09 |
77.07 |
|
rexnet_x10 |
77.38 |
|
rexnet_x13 |
79.06 |
|
rexnet_x15 |
79.94 |
|
rexnet_x20 |
80.64 |
|
resnest50 |
80.81 |
|
resnest101 |
82.50 |
|
res2net50 |
79.35 |
|
res2net101 |
79.56 |
|
res2net50_v1b |
80.32 |
|
res2net101_v1b |
95.41 |
|
googlenet |
72.68 |
|
inceptionv3 |
79.11 |
|
inceptionv4 |
80.88 |
|
mobilenet_v1_025 |
53.87 |
|
mobilenet_v1_050 |
65.94 |
|
mobilenet_v1_075 |
70.44 |
|
mobilenet_v1_100 |
72.95 |
|
mobilenet_v2_075 |
69.98 |
|
mobilenet_v2_100 |
72.27 |
|
mobilenet_v2_140 |
75.56 |
|
mobilenet_v3_small |
68.10 |
|
mobilenet_v3_large |
75.23 |
|
shufflenet_v1_g3_x0_5 |
57.05 |
|
shufflenet_v1_g3_x1_5 |
67.77 |
|
shufflenet_v2_x0_5 |
57.05 |
|
shufflenet_v2_x1_0 |
67.77 |
|
shufflenet_v2_x1_5 |
57.05 |
|
shufflenet_v2_x2_0 |
67.77 |
|
xception |
79.01 |
|
ghostnet_50 |
66.03 |
|
ghostnet_100 |
73.78 |
|
ghostnet_130 |
75.50 |
|
nasnet_a_4x1056 |
73.65 |
|
mnasnet_0.5 |
68.07 |
|
mnasnet_0.75 |
71.81 |
|
mnasnet_1.0 |
74.28 |
|
mnasnet_1.4 |
76.01 |
|
efficientnet_b0 |
76.89 |
|
efficientnet_b1 |
78.95 |
|
regnet_x_200mf |
68.74 |
|
regnet_x_400mf |
73.16 |
|
regnet_x_600mf |
73.34 |
|
regnet_x_800mf |
76.04 |
|
regnet_y_200mf |
70.30 |
|
regnet_y_400mf |
73.91 |
|
regnet_y_600mf |
75.69 |
|
regnet_y_800mf |
76.52 |
|
mixnet_s |
75.52 |
|
mixnet_m |
76.64 |
|
mixnet_l |
78.73 |
|
hrnet_w32 |
80.64 |
|
hrnet_w48 |
81.19 |
|
bit_resnet50 |
76.81 |
|
bit_resnet50x3 |
80.63 |
|
bit_resnet101 |
77.93 |
|
repvgg_a0 |
72.19 |
|
repvgg_a1 |
74.19 |
|
repvgg_a2 |
76.63 |
|
repvgg_b0 |
74.99 |
|
repvgg_b1 |
78.81 |
|
repvgg_b2 |
79.29 |
|
repvgg_b3 |
80.46 |
|
repvgg_b1g2 |
78.03 |
|
repvgg_b1g4 |
77.64 |
|
repvgg_b2g4 |
78.80 |
|
repmlp_t224 |
76.71 |
|
convnext_tiny |
81.91 |
|
convnext_small |
83.40 |
|
convnext_base |
83.32 |
|
vit_b_32_224 |
75.86 |
|
vit_l_16_224 |
76.34 |
|
vit_l_32_224 |
73.71 |
|
swintransformer_tiny |
80.82 |
|
pvt_tiny |
74.81 |
|
pvt_small |
79.66 |
|
pvt_medium |
81.82 |
|
pvt_large |
81.75 |
|
pvt_v2_b0 |
71.50 |
|
pvt_v2_b1 |
78.91 |
|
pvt_v2_b2 |
81.99 |
|
pvt_v2_b3 |
82.84 |
|
pvt_v2_b4 |
83.14 |
|
pit_ti |
72.96 |
|
pit_xs |
78.41 |
|
pit_s |
80.56 |
|
pit_b |
81.87 |
|
coat_lite_tiny |
77.35 |
|
coat_lite_mini |
78.51 |
|
coat_tiny |
79.67 |
|
convit_tiny |
73.66 |
|
convit_tiny_plus |
77.00 |
|
convit_small |
81.63 |
|
convit_small_plus |
81.80 |
|
convit_base |
82.10 |
|
convit_base_plus |
81.96 |
|
crossvit_9 |
73.56 |
|
crossvit_15 |
81.08 |
|
crossvit_18 |
81.93 |
|
mobilevit_xx_small |
68.90 |
|
mobilevit_x_small |
74.98 |
|
mobilevit_small |
78.48 |
|
visformer_tiny |
78.28 |
|
visformer_tiny_v2 |
78.82 |
|
visformer_small |
81.76 |
|
visformer_small_v2 |
82.17 |
|
edgenext_xx_small |
71.02 |
|
edgenext_x_small |
75.14 |
|
edgenext_small |
79.15 |
|
edgenext_base |
82.24 |
|
poolformer_s12 |
77.33 |
|
xcit_tiny_12_p16 |
77.67 |
OCR
文本检测
model |
dataset |
fscore |
mindocr |
---|---|---|---|
dbnet_mobilenetv3 |
icdar2015 |
77.23 |
|
dbnet_resnet18 |
icdar2015 |
81.73 |
|
dbnet_resnet50 |
icdar2015 |
85.05 |
|
dbnet++_resnet50 |
icdar2015 |
86.74 |
|
psenet_resnet152 |
icdar2015 |
82.06 |
|
east_resnet50 |
icdar2015 |
84.87 |
|
fcenet_resnet50 |
icdar2015 |
84.12 |
文本识别
model |
dataset |
acc |
mindocr |
---|---|---|---|
svtr_tiny |
IC03,13,15,IIIT,etc |
89.02 |
|
crnn_vgg7 |
IC03,13,15,IIIT,etc |
82.03 |
|
crnn_resnet34_vd |
IC03,13,15,IIIT,etc |
84.45 |
|
rare_resnet34_vd |
IC03,13,15,IIIT,etc |
85.19 |
文本方向分类
model |
dataset |
acc |
mindocr |
---|---|---|---|
mobilenetv3 |
RCTW17,MTWI,LSVT |
94.59 |
目标检测
YOLO系列
model |
map |
mindyolo |
---|---|---|
yolov8_n |
37.2 |
|
yolov8_s |
44.6 |
|
yolov8_m |
50.5 |
|
yolov8_l |
52.8 |
|
yolov8_x |
53.7 |
|
yolov7_t |
37.5 |
|
yolov7_l |
50.8 |
|
yolov7_x |
52.4 |
|
yolov5_n |
27.3 |
|
yolov5_s |
37.6 |
|
yolov5_m |
44.9 |
|
yolov5_l |
48.5 |
|
yolov5_x |
50.5 |
|
yolov4_csp |
45.4 |
|
yolov4_csp(silu) |
45.8 |
|
yolov3_darknet53 |
45.5 |
|
yolox_n |
24.1 |
|
yolox_t |
33.3 |
|
yolox_s |
40.7 |
|
yolox_m |
46.7 |
|
yolox_l |
49.2 |
|
yolox_x |
51.6 |
|
yolox_darknet53 |
47.7 |
强化学习
算法 |
离散行动空间 |
连续行动空间 |
CPU |
GPU |
Ascend |
环境 |
Reward |
---|---|---|---|---|---|---|---|
✅ |
/ |
✅ |
✅ |
✅ |
195 |
||
/ |
✅ |
✅ |
✅ |
✅ |
4800 |
||
✅ |
/ |
✅ |
✅ |
✅ |
195 |
||
✅ |
/ |
✅ |
✅ |
✅ |
195 |
||
/ |
✅ |
✅ |
✅ |
✅ |
4800 |
||
✅ |
/ |
✅ |
✅ |
✅ |
90%/-145 |
||
/ |
✅ |
✅ |
✅ |
✅ |
4800 |
||
/ |
✅ |
✅ |
✅ |
✅ |
4800 |
||
✅ |
/ |
✅ |
✅ |
✅ |
195 |
||
✅ |
/ |
/ |
✅ |
✅ |
195 |
||
/ |
✅ |
✅ |
✅ |
✅ |
3500 |
||
✅ |
/ |
✅ |
✅ |
✅ |
-145 |
||
/ |
✅ |
✅ |
✅ |
✅ |
4800 |
||
/ |
✅ |
✅ |
✅ |
✅ |
5000 |
||
/ |
✅ |
/ |
✅ |
✅ |
900 |
||
/ |
✅ |
✅ |
✅ |
✅ |
3000 |
||
✅ |
/ |
✅ |
✅ |
✅ |
-140 |
||
✅ |
/ |
✅ |
✅ |
✅ |
195 |
||
✅ |
/ |
✅ |
✅ |
✅ |
195 |
||
✅ |
/ |
✅ |
✅ |
✅ |
195 |
推荐
model |
dataset |
auc |
mindrec |
mindspore |
---|---|---|---|---|
Wide&Deep |
Criteo |
0.8 |
||
Deep&Cross Network (DCN) |
Criteo |
0.8 |
科学计算套件
领域 |
网络 |
MindSpore实现 |
Ascend |
GPU |
---|---|---|---|---|
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
|||
通用物理 |
✅ |
|||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
|||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
|||
通用物理 |
✅ |
|||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
通用物理 |
✅ |
✅ |
||
哈密顿系统 |
✅ |
✅ |
||
弹性动力学 |
✅ |
✅ |
||
热力学 |
✅ |
✅ |
||
气象学 |
✅ |
✅ |
||
地质学 |
✅ |
✅ |
||
地质学 |
✅ |
✅ |
||
海洋物理 |
✅ |
|||
海洋物理 |
✅ |
✅ |
||
电磁学 |
✅ |
✅ |
||
电磁学 |
✅ |
✅ |
||
电磁学 |
✅ |
✅ |
||
电磁学 |
✅ |
✅ |
||
电磁学 |
✅ |
|||
电磁学 |
✅ |
✅ |
||
电磁学 |
✅ |
✅ |
||
电磁学 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算生物 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |
||
计算流体 |
✅ |
✅ |