shadow-cann commited on
Commit
aa77156
·
verified ·
1 Parent(s): 73d8918

Add files using upload-large-folder tool

Browse files
1719967754027009_combined_plot.jpg ADDED
README.md ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - zh
4
+ tags:
5
+ - hisilicon
6
+ - hispark
7
+ - npu
8
+ - openharmony
9
+ - modelzoo
10
+ - pytorch
11
+ ---
12
+
13
+ # CrowdCount
14
+
15
+ CrowdCount是一种基于多尺度卷积神经网络(MSCNN)的高精度人群计数模型。相比传统多列 / 多网络方法,它通过单列网络中的多尺度特征块(MSB)与尺度自适应密度图回归技术,能有效应对透视畸变导致的人物尺度差异问题,兼顾计数精度与模型轻量化,适用于监控图像、公共场所等密集人群计数场景。
16
+
17
+ ## Mirror Metadata
18
+
19
+ - Hugging Face repo: shadow-cann/hispark-modelzoo-crowdcount
20
+ - Portal model id: i9i4tr9hec00
21
+ - Created at: 2025-12-25 15:03:50
22
+ - Updated at: 2025-12-30 21:27:21
23
+ - Category: 计算机视觉
24
+
25
+ ## Framework
26
+
27
+ - PyTorch
28
+
29
+ ## Supported OS
30
+
31
+ - OpenHarmony
32
+ - Linux
33
+
34
+ ## Computing Power
35
+
36
+ - Hi3403V100 SVP_NNN
37
+ - Hi3403V100 NNN
38
+
39
+ ## Tags
40
+
41
+ - 人群计数
42
+
43
+ ## Detail Parameters
44
+
45
+ - 输入: 224x224
46
+ - 参数量: 34.738M
47
+ - 计算量: 587.807GFLOPs
48
+
49
+ ## Files In This Repo
50
+
51
+ - mscnn_model.onnx (源模型 / 源模型下载; 源模型 / 源模型元数据)
52
+ - SVP_NNN_PC_V1.0.6.0.tgz (附加资源 / 附加资源)
53
+
54
+ ## Upstream Links
55
+
56
+ - Portal card: https://gitbubble.github.io/hisilicon-developer-portal-mirror/model-detail.html?id=i9i4tr9hec00
57
+ - Upstream repository: https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/count/CrowdCount
58
+ - License reference: https://github.com/zzubqh/CrowdCount/blob/master/LICENSE
59
+
60
+ ## Notes
61
+
62
+ - This repository was mirrored from the HiSilicon Developer Portal model card and local downloads captured on 2026-03-27.
63
+ - File ownership follows the portal card mapping, not just filename similarity.
64
+ - Cover image: 1719967754027009_combined_plot.jpg
SVP_NNN_PC_V1.0.6.0.tgz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:536b103d08e9490f968207326798e1fc50ea4234888b73db729cd6e6a04c5d8b
3
+ size 31072256
model-card.json ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "name": "CrowdCount",
3
+ "id": "i9i4tr9hec00",
4
+ "description": "CrowdCount是一种基于多尺度卷积神经网络(MSCNN)的高精度人群计数模型。相比传统多列 / 多网络方法,它通过单列网络中的多尺度特征块(MSB)与尺度自适应密度图回归技术,能有效应对透视畸变导致的人物尺度差异问题,兼顾计数精度与模型轻量化,适用于监控图像、公共场所等密集人群计数场景。",
5
+ "category": "计算机视觉",
6
+ "framework": [
7
+ "PyTorch"
8
+ ],
9
+ "supportOs": [
10
+ "OpenHarmony",
11
+ "Linux"
12
+ ],
13
+ "computingPower": [
14
+ "Hi3403V100 SVP_NNN",
15
+ "Hi3403V100 NNN"
16
+ ],
17
+ "tags": [
18
+ "人群计数"
19
+ ],
20
+ "repositoryUrl": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/count/CrowdCount",
21
+ "licenseUrl": "https://github.com/zzubqh/CrowdCount/blob/master/LICENSE",
22
+ "downloads": [
23
+ {
24
+ "fileName": "mscnn_model.onnx",
25
+ "variants": [
26
+ "源模型 / 源模型下载",
27
+ "源模型 / 源模型元数据"
28
+ ]
29
+ },
30
+ {
31
+ "fileName": "SVP_NNN_PC_V1.0.6.0.tgz",
32
+ "variants": [
33
+ "附加资源 / 附加资源"
34
+ ]
35
+ }
36
+ ],
37
+ "apiDetail": {
38
+ "createdBy": 139318985286440,
39
+ "creationDate": "2025-12-25 15:03:50",
40
+ "creationUserCN": "sloanqin",
41
+ "lastUpdatedBy": null,
42
+ "lastUpdateDate": "2025-12-30 21:27:21",
43
+ "lastUpdateUserCN": "sloanqin",
44
+ "rowIdx": -1,
45
+ "id": "i9i4tr9hec00",
46
+ "name": "CrowdCount",
47
+ "isBeta": 0,
48
+ "betaVersionDesc": "",
49
+ "description": "CrowdCount是一种基于多尺度卷积神经网络(MSCNN)的高精度人群计数模型。相比传统多列 / 多网络方法,它通过单列网络中的多尺度特征块(MSB)与尺度自适应密度图回归技术,能有效应对透视畸变导致的人物尺度差异问题,兼顾计数精度与模型轻量化,适用于监控图像、公共场所等密集人群计数场景。",
50
+ "parentId": "i9i4tr9hec00",
51
+ "coverImageId": 1719967754027009,
52
+ "coverImageUrl": "https://openxinhuo-board-image.obs.cn-east-3.myhuaweicloud.com/1719967754027009%2Fcombined_plot.jpg",
53
+ "modelEffectId": null,
54
+ "modelEffectUrl": "",
55
+ "computerVersion": [
56
+ "人群计数"
57
+ ],
58
+ "naturalLanguageProcess": [],
59
+ "multimodal": [],
60
+ "video": [],
61
+ "framework": [
62
+ "PyTorch"
63
+ ],
64
+ "modelRepository": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/count/CrowdCount",
65
+ "originModel": [
66
+ {
67
+ "id": "1719582624645123",
68
+ "name": "mscnn_model.onnx",
69
+ "url": null,
70
+ "size": "138968748"
71
+ }
72
+ ],
73
+ "originModelLink": null,
74
+ "dataSet": "https://pan.baidu.com/s/1T5EfBovMnpe4meIYcXSa8w",
75
+ "modelLicense": "https://github.com/zzubqh/CrowdCount/blob/master/LICENSE",
76
+ "detailParams": [
77
+ {
78
+ "name": "输入",
79
+ "value": "224x224"
80
+ },
81
+ {
82
+ "name": "参数量",
83
+ "value": "34.738M"
84
+ },
85
+ {
86
+ "name": "计算量",
87
+ "value": "587.807GFLOPs"
88
+ }
89
+ ],
90
+ "quickStart": {
91
+ "url": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/count/CrowdCount",
92
+ "markDownUrl": null,
93
+ "developLanguage": [
94
+ {
95
+ "language": "C++",
96
+ "context": "{\"ops\":[{\"attributes\":{\"background\":\"#ffffff\",\"size\":\"16px\",\"color\":\"#40485b\",\"line-height\":\"1.6\"},\"insert\":\"模型可以通过以下代码完成快速推理\"},{\"attributes\":{\"text-indent\":\"0px\"},\"insert\":\"\\n\"},{\"insert\":\"#include \\\"model.h\\\"\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"#include \\\"log.h\\\"\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"using namespace Infer;\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"int main()\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"{\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    EnvInit();\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    std::string omModelPath = \\\"/path/to/model.om\\\"; // 模型文件路径 \"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    std::string imagePath = \\\"/path/to/image.jpg\\\"; // 输入图片路径\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    std::unique_ptr<Model> model = std::make_unique<Model>();\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    if (model->Load(omModelPath, ModelType::CrowdCount) != 0) {\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        LOG(ERROR) << \\\"fail to load model\\\";\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        return -1;\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    }\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    auto ret = model->Infer(imagePath, FileType::SingelImageFile);\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    if (ret.size() == 0) {\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        LOG(ERROR) << \\\"fail to infer model\\\";\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        model->Unload();\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        return -1;\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    }\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    if (model->Unload() != 0) {\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        LOG(ERROR) << \\\"fail to unload model\\\";\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"        return -1;\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    }\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    EnvDeinit();\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    return 0;\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"}\"},{\"attributes\":{\"text-indent\":\"0px\",\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"备注:上述C++代码依赖的动态库与头文件位于\"},{\"attributes\":{\"link\":\"https://gitee.com/HiSpark/modelzoo/tree/master/samples/common\"},\"insert\":\"/samples/common\"},{\"insert\":\"目录下,编译相关配置参考\"},{\"attributes\":{\"link\":\"https://gitee.com/HiSpark/modelzoo/blob/master/samples/built-in/count/CrowdCount/src/CMakeLists.txt\"},\"insert\":\"CMakeLists.txt\"},{\"insert\":\"。\\n\"}]}"
97
+ }
98
+ ]
99
+ },
100
+ "status": "released",
101
+ "currentHandler": "",
102
+ "currentHandlerName": "",
103
+ "jsonPath": "https://gitee.com/HiSpark/modelzoo-dev/blob/master/samples/built-in/count/CrowdCount/crowdcount.json",
104
+ "modelAdaptor": [
105
+ {
106
+ "createdBy": null,
107
+ "creationDate": null,
108
+ "creationUserCN": null,
109
+ "lastUpdatedBy": null,
110
+ "lastUpdateDate": null,
111
+ "lastUpdateUserCN": null,
112
+ "rowIdx": -1,
113
+ "id": "i8ttm5k1tc00",
114
+ "name": "Hi3403V100 SVP_NNN",
115
+ "modelId": "i9i4tr9hec00",
116
+ "modelName": "CrowdCount",
117
+ "supportNames": [
118
+ "a16w8"
119
+ ],
120
+ "toolkit": [
121
+ {
122
+ "name": "CANN工具",
123
+ "url": "https://hispark-obs.obs.cn-east-3.myhuaweicloud.com/SVP_NNN_PC_V1.0.6.0.tgz",
124
+ "desc": "Al异构计算架构;提升计算效率的关键平台",
125
+ "imgId": "cann"
126
+ },
127
+ {
128
+ "name": "编译工具链",
129
+ "url": "https://gitee.com/HiSpark/pegasus/blob/Beta-v0.9.1/docs/Hi3403V100%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA%E6%8C%87%E5%8D%97/Hi3403V100%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA%E6%8C%87%E5%8D%97.md",
130
+ "desc": "高效编译,精准适配;AI性能优化,应用流畅运行",
131
+ "imgId": "tool"
132
+ },
133
+ {
134
+ "name": "SDK",
135
+ "url": "https://gitee.com/HiSpark/ss928v100_clang/tree/Beta-v0.9.1/",
136
+ "desc": "稳定、易用的设计;支撑客户快速产品量产",
137
+ "imgId": "sdk"
138
+ }
139
+ ],
140
+ "supportOs": [
141
+ "OpenHarmony",
142
+ "Linux"
143
+ ],
144
+ "supportQuantify": [
145
+ {
146
+ "createdBy": 132241120926760,
147
+ "creationDate": "2026-03-26 20:22:57",
148
+ "creationUserCN": "liaoshibin",
149
+ "lastUpdatedBy": 132241120926760,
150
+ "lastUpdateDate": "2026-03-26 20:22:57",
151
+ "lastUpdateUserCN": "liaoshibin",
152
+ "rowIdx": -1,
153
+ "id": "j6tmkobsi000",
154
+ "name": "a16w8",
155
+ "computingId": "i8ttm5k1tc00",
156
+ "computingName": "Hi3403V100 SVP_NNN",
157
+ "omOfflineModelUrl": null,
158
+ "omOfflineModelId": 1719582666588163,
159
+ "omOfflineModelSize": "34998884",
160
+ "omOfflineModelName": "mscnn_model_dpico.om",
161
+ "omOfflineModel": [
162
+ {
163
+ "id": "1719582666588163",
164
+ "name": "mscnn_model_dpico.om",
165
+ "url": null,
166
+ "size": "34998884"
167
+ }
168
+ ],
169
+ "omOfflineModelLink": null,
170
+ "releaseTime": "2025-12-31",
171
+ "boardOs": null,
172
+ "modelLicense": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/count/CrowdCount/LICENSE",
173
+ "modelPerformance": [
174
+ {
175
+ "performanceValue": "217.01",
176
+ "unit": "耗时(ms)",
177
+ "desc": null
178
+ },
179
+ {
180
+ "performanceValue": "4.61",
181
+ "unit": "性能(fps)",
182
+ "desc": ""
183
+ },
184
+ {
185
+ "performanceValue": "322.604",
186
+ "unit": "单帧内存带宽(MB)",
187
+ "desc": ""
188
+ },
189
+ {
190
+ "performanceValue": "67.703",
191
+ "unit": "内存(MB)",
192
+ "desc": ""
193
+ }
194
+ ],
195
+ "deleted": 0
196
+ }
197
+ ],
198
+ "deleted": 0
199
+ },
200
+ {
201
+ "createdBy": null,
202
+ "creationDate": null,
203
+ "creationUserCN": null,
204
+ "lastUpdatedBy": null,
205
+ "lastUpdateDate": null,
206
+ "lastUpdateUserCN": null,
207
+ "rowIdx": -1,
208
+ "id": "i8ttm5k1tc01",
209
+ "name": "Hi3403V100 NNN",
210
+ "modelId": "i9i4tr9hec00",
211
+ "modelName": "CrowdCount",
212
+ "supportNames": [
213
+ "f16"
214
+ ],
215
+ "toolkit": [
216
+ {
217
+ "name": "CANN工具包",
218
+ "url": "",
219
+ "desc": "5.30.t11.7.b110; (请联系FAE获取)",
220
+ "imgId": "cann"
221
+ },
222
+ {
223
+ "name": "编译工具链",
224
+ "url": "",
225
+ "desc": "aarch64-mix210-linux-gcc;(请联系FAE获取)",
226
+ "imgId": "tool"
227
+ },
228
+ {
229
+ "name": "SDK",
230
+ "url": "",
231
+ "desc": "SS928 V100R001C02SPC022; (请联系FAE获取)",
232
+ "imgId": "sdk"
233
+ }
234
+ ],
235
+ "supportOs": [
236
+ "Linux"
237
+ ],
238
+ "supportQuantify": [
239
+ {
240
+ "createdBy": 132241120926760,
241
+ "creationDate": "2026-03-26 20:22:57",
242
+ "creationUserCN": "liaoshibin",
243
+ "lastUpdatedBy": 132241120926760,
244
+ "lastUpdateDate": "2026-03-26 20:22:57",
245
+ "lastUpdateUserCN": "liaoshibin",
246
+ "rowIdx": -1,
247
+ "id": "j6tmkod8i000",
248
+ "name": "f16",
249
+ "computingId": "i8ttm5k1tc01",
250
+ "computingName": "Hi3403V100 NNN",
251
+ "omOfflineModelUrl": null,
252
+ "omOfflineModelId": 1719583465603074,
253
+ "omOfflineModelSize": "70287392",
254
+ "omOfflineModelName": "mscnn_model_dlite.om",
255
+ "omOfflineModel": [
256
+ {
257
+ "id": "1719583465603074",
258
+ "name": "mscnn_model_dlite.om",
259
+ "url": null,
260
+ "size": "70287392"
261
+ }
262
+ ],
263
+ "omOfflineModelLink": null,
264
+ "releaseTime": "2025-12-31",
265
+ "boardOs": null,
266
+ "modelLicense": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/count/CrowdCount/LICENSE",
267
+ "modelPerformance": [
268
+ {
269
+ "performanceValue": "349.99",
270
+ "unit": "耗时(ms)",
271
+ "desc": null
272
+ },
273
+ {
274
+ "performanceValue": "2.86",
275
+ "unit": "性能(fps)",
276
+ "desc": ""
277
+ },
278
+ {
279
+ "performanceValue": "1677.176",
280
+ "unit": "单帧内存带宽(MB)",
281
+ "desc": ""
282
+ },
283
+ {
284
+ "performanceValue": "243.684",
285
+ "unit": "内存(MB)",
286
+ "desc": ""
287
+ }
288
+ ],
289
+ "deleted": 0
290
+ }
291
+ ],
292
+ "deleted": 0
293
+ }
294
+ ],
295
+ "saveType": null,
296
+ "deleteType": null,
297
+ "latest": "Y",
298
+ "deleted": 0,
299
+ "modelPhase": "released",
300
+ "remark": null,
301
+ "fileInfo": null,
302
+ "reviewType": null,
303
+ "owner": "sloanqin",
304
+ "ownerBy": 139318985286440,
305
+ "optional": null,
306
+ "optionalList": [
307
+ "0e900d99dee8461b8",
308
+ "whalenowings"
309
+ ],
310
+ "optionalBy": null,
311
+ "downloadNum": 7,
312
+ "collectNum": null,
313
+ "isCollect": null
314
+ }
315
+ }
mscnn_model.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c96fab5012852e081ea98a43f587e0789eaa126ba104fd1f4c9fc210784a5b2b
3
+ size 138968748