shadow-cann commited on
Commit
d8df29a
·
verified ·
1 Parent(s): b217f6f

Add files using upload-large-folder tool

Browse files
.gitattributes CHANGED
@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ yolov5s.om filter=lfs diff=lfs merge=lfs -text
37
+ yolov5s_om-A8W8.om filter=lfs diff=lfs merge=lfs -text
38
+ reid_net_om-A8W8.om filter=lfs diff=lfs merge=lfs -text
39
+ 1736139606130691_20260326235720.jpg filter=lfs diff=lfs merge=lfs -text
40
+ reid_net.om filter=lfs diff=lfs merge=lfs -text
1736139606130691_20260326235720.jpg ADDED

Git LFS Details

  • SHA256: 1097aec0778c72ac617a845ef28028f928b886415df2538c2646ea0d2a2b0fbe
  • Pointer size: 131 Bytes
  • Size of remote file: 202 kB
README.md ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - zh
4
+ tags:
5
+ - hisilicon
6
+ - hispark
7
+ - npu
8
+ - openharmony
9
+ - modelzoo
10
+ - pytorch
11
+ ---
12
+
13
+ # DeepSort
14
+
15
+ DeepSort是一种多目标跟踪方法,简单有效。该方法将外观信息集成起来,提高了分拣性能,能够在较长遮挡时间下仍能进行有效的跟踪。该框架将大量的复杂计算放入离线预训练阶段,这个阶段在重识别数据集上学习一个深度关联度量。在线应用阶段,建立度量,在视觉外观空间中使用最近邻查询跟踪关联。本模型能够在较快帧率下实现较高精度的识别。
16
+
17
+ ## Mirror Metadata
18
+
19
+ - Hugging Face repo: shadow-cann/hispark-modelzoo-deepsort
20
+ - Portal model id: j6v87p1oi000
21
+ - Created at: 2026-03-26 23:59:38
22
+ - Updated at: 2026-03-27 19:43:15
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
+ - 多目标跟踪
42
+
43
+ ## Detail Parameters
44
+
45
+ - 计算量: 2.253GFLOPs
46
+ - 输入: 128x64
47
+ - 参数量: 11.164M
48
+
49
+ ## Files In This Repo
50
+
51
+ - yolov5s_om-A8W8.om (编译模型 / A8W8)
52
+ - reid_net_om-A8W8.om (编译模型 / A8W8)
53
+ - yolov5s.om (编译模型 / FP16; 编译模型 / OM 元数据 / A8W8)
54
+ - reid_net.om (编译模型 / FP16; 编译模型 / OM 元数据 / A8W8)
55
+ - yolov5s.onnx (源模型 / 源模型下载; 源模型 / 源模型元数据)
56
+ - reid_net.onnx (源模型 / 源模型下载; 源模型 / 源模型元数据)
57
+
58
+ ## Upstream Links
59
+
60
+ - Portal card: https://gitbubble.github.io/hisilicon-developer-portal-mirror/model-detail.html?id=j6v87p1oi000
61
+ - Upstream repository: https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort
62
+ - License reference: https://github.com/ZQPei/deep_sort_pytorch/blob/master/LICENSE
63
+
64
+ ## Notes
65
+
66
+ - This repository was mirrored from the HiSilicon Developer Portal model card and local downloads captured on 2026-03-27.
67
+ - File ownership follows the portal card mapping, not just filename similarity.
68
+ - Cover image: 1736139606130691_20260326235720.jpg
model-card.json ADDED
@@ -0,0 +1,348 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "name": "DeepSort",
3
+ "id": "j6v87p1oi000",
4
+ "description": "DeepSort是一种多目标跟踪方法,简单有效。该方法将外观信息集成起来,提高了分拣性能,能够在较长遮挡时间下仍能进行有效的跟踪。该框架将大量的复杂计算放入离线预训练阶段,这个阶段在重识别数据集上学习一个深度关联度量。在线应用阶段,建立度量,在视觉外观空间中使用最近邻查询跟踪关联。本模型能够在较快帧率下实现较高精度的识别。",
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
+ "多目标跟踪"
19
+ ],
20
+ "repositoryUrl": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort",
21
+ "licenseUrl": "https://github.com/ZQPei/deep_sort_pytorch/blob/master/LICENSE",
22
+ "downloads": [
23
+ {
24
+ "fileName": "yolov5s_om-A8W8.om",
25
+ "variants": [
26
+ "编译模型 / A8W8"
27
+ ]
28
+ },
29
+ {
30
+ "fileName": "reid_net_om-A8W8.om",
31
+ "variants": [
32
+ "编译模型 / A8W8"
33
+ ]
34
+ },
35
+ {
36
+ "fileName": "yolov5s.om",
37
+ "variants": [
38
+ "编译模型 / FP16",
39
+ "编译模型 / OM 元数据 / A8W8"
40
+ ]
41
+ },
42
+ {
43
+ "fileName": "reid_net.om",
44
+ "variants": [
45
+ "编译模型 / FP16",
46
+ "编译模型 / OM 元数据 / A8W8"
47
+ ]
48
+ },
49
+ {
50
+ "fileName": "yolov5s.onnx",
51
+ "variants": [
52
+ "源模型 / 源模型下载",
53
+ "源模型 / 源模型元数据"
54
+ ]
55
+ },
56
+ {
57
+ "fileName": "reid_net.onnx",
58
+ "variants": [
59
+ "源模型 / 源模型下载",
60
+ "源模型 / 源模型元数据"
61
+ ]
62
+ }
63
+ ],
64
+ "apiDetail": {
65
+ "createdBy": 137441035408492,
66
+ "creationDate": "2026-03-26 23:59:38",
67
+ "creationUserCN": "0e900d99dee8461b8",
68
+ "lastUpdatedBy": null,
69
+ "lastUpdateDate": "2026-03-27 19:43:15",
70
+ "lastUpdateUserCN": "0e900d99dee8461b8",
71
+ "rowIdx": -1,
72
+ "id": "j6v87p1oi000",
73
+ "name": "DeepSort",
74
+ "isBeta": 0,
75
+ "betaVersionDesc": "",
76
+ "description": "DeepSort是一种多目标跟踪方法,简单有效。该方法将外观信息集成起来,提高了分拣性能,能够在较长遮挡时间下仍能进行有效的跟踪。该框架将大量的复杂计算放入离线预训练阶段,这个阶段在重识别数据集上学习一个深度关联度量。在线应用阶段,建立度量,在视觉外观空间中使用最近邻查询跟踪关联。本模型能够在较快帧率下实现较高精度的识别。",
77
+ "parentId": "j6v87p1oi000",
78
+ "coverImageId": 1736139606130691,
79
+ "coverImageUrl": "https://openxinhuo-board-image.obs.cn-east-3.myhuaweicloud.com/1736139606130691%2F20260326235720.jpg",
80
+ "modelEffectId": 1736210009620483,
81
+ "modelEffectUrl": "https://openxinhuo-board-image.obs.cn-east-3.myhuaweicloud.com/1736210009620483%2Ffinal.mp4",
82
+ "computerVersion": [
83
+ "多目标跟踪"
84
+ ],
85
+ "naturalLanguageProcess": [],
86
+ "multimodal": [],
87
+ "video": [],
88
+ "framework": [
89
+ "PyTorch"
90
+ ],
91
+ "modelRepository": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort",
92
+ "originModel": [
93
+ {
94
+ "id": "1736138880516099",
95
+ "name": "yolov5s.onnx",
96
+ "url": null,
97
+ "size": "28944024"
98
+ },
99
+ {
100
+ "id": "1736138880516098",
101
+ "name": "reid_net.onnx",
102
+ "url": null,
103
+ "size": "44662689"
104
+ }
105
+ ],
106
+ "originModelLink": "",
107
+ "dataSet": "",
108
+ "modelLicense": "https://github.com/ZQPei/deep_sort_pytorch/blob/master/LICENSE",
109
+ "detailParams": [
110
+ {
111
+ "name": "计算量",
112
+ "value": "2.253GFLOPs"
113
+ },
114
+ {
115
+ "name": "输入",
116
+ "value": "128x64"
117
+ },
118
+ {
119
+ "name": "参数量",
120
+ "value": "11.164M"
121
+ }
122
+ ],
123
+ "quickStart": {
124
+ "url": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort",
125
+ "markDownUrl": "",
126
+ "developLanguage": [
127
+ {
128
+ "language": "C++",
129
+ "context": "{\"ops\":[{\"attributes\":{\"line-height\":\"19px\",\"size\":\"14px\",\"background\":\"#ffffff\",\"color\":\"#3b3b3b\"},\"insert\":\"模型可以通过以下代码完成快速推理\"},{\"insert\":\"\\nint main(int argc, char **argv) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  if (argc < 4) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    std::cerr << \\\"Usage: ./main <yolov5.om> <resnet18.om> \\\"\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"           \\\"<image_dir_or_file> [config.json]\\\" << std::endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    return -1;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"  // 初始化 NPU 驱动设备\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  if (Infer::DevInit(\\\"\\\") != Infer::SUCCESS) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    std::cerr << \\\"Device Init Failed.\\\" << std::endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    return -1;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"  // 构建默认超参数配置\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  DeepSORTConfig config;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  config.yoloModelPath = argv[1];\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  config.resnetModelPath = argv[2];\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  config.yoloConfThres = 0.3f;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  config.yoloNmsThres = 0.4f;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  config.maxCosineDistance = 0.15f;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  config.nnBudget = 100;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"  // 卡尔曼滤波器数学对齐自检\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  RunKalmanParityCheck();\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  std::string configPath = (argc > 4) ? argv[4] : \\\"\\\";\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  if (!configPath.empty()) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    LoadConfigFromJson(configPath, config);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"  std::string inputPath = argv[3];\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  std::string outFilename = BuildOutputFilename(inputPath);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    DeepSortController deepSort(config);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    if (deepSort.Init() != Infer::SUCCESS) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"      std::cerr << \\\"DeepSort Init Failed.\\\" << std::endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"      Infer::DevDeInit();\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"      return -1;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"    std::vector<std::string> framePaths = GetFramePaths(inputPath);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    if (framePaths.empty()) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"      std::cerr << \\\"No image files found in: \\\" << inputPath << std::endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"      Infer::DevDeInit();\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"      return -1;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"    }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"    RunTrackingPipeline(deepSort, framePaths, outFilename);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"  Infer::DevDeInit();\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"  return 0;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"}\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"attributes\":{\"line-height\":\"19px\",\"size\":\"14px\",\"background\":\"#ffffff\",\"color\":\"#3b3b3b\"},\"insert\":\"备注:上述C++代码依赖的动态库与头文件位于\"},{\"attributes\":{\"line-height\":\"19px\",\"size\":\"14px\",\"background\":\"#ffffff\",\"color\":\"#a31515\",\"link\":\"https://gitee.com/HiSpark/modelzoo/tree/master/samples/common\"},\"insert\":\"/samples/common\"},{\"attributes\":{\"line-height\":\"19px\",\"size\":\"14px\",\"background\":\"#ffffff\",\"color\":\"#3b3b3b\"},\"insert\":\"目录下,编译相关配置参考\"},{\"attributes\":{\"line-height\":\"19px\",\"size\":\"14px\",\"background\":\"#ffffff\",\"color\":\"#a31515\",\"link\":\"https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort/CMakeLists.txt\"},\"insert\":\"CMakeLists.txt\"},{\"attributes\":{\"line-height\":\"19px\",\"size\":\"14px\",\"background\":\"#ffffff\",\"color\":\"#3b3b3b\"},\"insert\":\"。\"},{\"insert\":\"\\n\"}]}"
130
+ }
131
+ ]
132
+ },
133
+ "status": "released",
134
+ "currentHandler": "",
135
+ "currentHandlerName": "",
136
+ "jsonPath": "",
137
+ "modelAdaptor": [
138
+ {
139
+ "createdBy": null,
140
+ "creationDate": null,
141
+ "creationUserCN": null,
142
+ "lastUpdatedBy": null,
143
+ "lastUpdateDate": null,
144
+ "lastUpdateUserCN": null,
145
+ "rowIdx": -1,
146
+ "id": "i8ttm5k1tc00",
147
+ "name": "Hi3403V100 SVP_NNN",
148
+ "modelId": "j6v87p1oi000",
149
+ "modelName": "DeepSort",
150
+ "supportNames": [
151
+ "A8W8"
152
+ ],
153
+ "toolkit": [
154
+ {
155
+ "name": "CANN工具",
156
+ "url": "https://gitee.com/link?target=https%3A%2F%2Fhispark-obs.obs.cn-east-3.myhuaweicloud.com%2FSVP_NNN_PC_V1.0.6.5.tgz",
157
+ "desc": "AI异构计算架构;提升计算效率的关键平台",
158
+ "imgId": "cann"
159
+ },
160
+ {
161
+ "name": "编译工具库",
162
+ "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",
163
+ "desc": "高效编译,精准适配;AI性能优化,应用流畅运行",
164
+ "imgId": "tool"
165
+ },
166
+ {
167
+ "name": "SDK",
168
+ "url": "https://gitee.com/HiSpark/ss928v100_clang/tree/Beta-v0.9.1/",
169
+ "desc": "稳定、易用的设计;支撑客户快速产品量产",
170
+ "imgId": "sdk"
171
+ }
172
+ ],
173
+ "supportOs": [
174
+ "OpenHarmony",
175
+ "Linux"
176
+ ],
177
+ "supportQuantify": [
178
+ {
179
+ "createdBy": 137441035408492,
180
+ "creationDate": "2026-03-27 09:19:59",
181
+ "creationUserCN": "0e900d99dee8461b8",
182
+ "lastUpdatedBy": 137441035408492,
183
+ "lastUpdateDate": "2026-03-27 09:19:59",
184
+ "lastUpdateUserCN": "0e900d99dee8461b8",
185
+ "rowIdx": -1,
186
+ "id": "j738fv6gi000",
187
+ "name": "A8W8",
188
+ "computingId": "i8ttm5k1tc00",
189
+ "computingName": "Hi3403V100 SVP_NNN",
190
+ "omOfflineModelUrl": null,
191
+ "omOfflineModelId": null,
192
+ "omOfflineModelSize": null,
193
+ "omOfflineModelName": null,
194
+ "omOfflineModel": [
195
+ {
196
+ "id": "1736139253809155",
197
+ "name": "yolov5s.om",
198
+ "url": null,
199
+ "size": "8626175"
200
+ },
201
+ {
202
+ "id": "1736139255906306",
203
+ "name": "reid_net.om",
204
+ "url": null,
205
+ "size": "11310387"
206
+ }
207
+ ],
208
+ "omOfflineModelLink": "",
209
+ "releaseTime": "2026-03-26",
210
+ "boardOs": "OpenHarmony",
211
+ "modelLicense": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort/LICENSE",
212
+ "modelPerformance": [
213
+ {
214
+ "performanceValue": "1.443",
215
+ "unit": "耗时(ms)",
216
+ "desc": ""
217
+ },
218
+ {
219
+ "performanceValue": "693.28",
220
+ "unit": "性能(fps)",
221
+ "desc": ""
222
+ },
223
+ {
224
+ "performanceValue": "59.863",
225
+ "unit": "内存(MB)",
226
+ "desc": ""
227
+ }
228
+ ],
229
+ "deleted": 0
230
+ }
231
+ ],
232
+ "deleted": 0
233
+ },
234
+ {
235
+ "createdBy": null,
236
+ "creationDate": null,
237
+ "creationUserCN": null,
238
+ "lastUpdatedBy": null,
239
+ "lastUpdateDate": null,
240
+ "lastUpdateUserCN": null,
241
+ "rowIdx": -1,
242
+ "id": "i8ttm5k1tc01",
243
+ "name": "Hi3403V100 NNN",
244
+ "modelId": "j6v87p1oi000",
245
+ "modelName": "DeepSort",
246
+ "supportNames": [
247
+ "A8W8",
248
+ "FP16"
249
+ ],
250
+ "toolkit": [
251
+ {
252
+ "name": "CANN工具",
253
+ "url": "",
254
+ "desc": "5.30.t11.7.b140;(请联系FAE获取)",
255
+ "imgId": "cann"
256
+ },
257
+ {
258
+ "name": "编译工具链",
259
+ "url": "",
260
+ "desc": "aarch64-mix210-linux-gcc;(请联系FAE获取)",
261
+ "imgId": "tool"
262
+ },
263
+ {
264
+ "name": "SDK",
265
+ "url": "",
266
+ "desc": "SPC022;(请联系FAE获取)",
267
+ "imgId": "sdk"
268
+ }
269
+ ],
270
+ "supportOs": [
271
+ "Linux"
272
+ ],
273
+ "supportQuantify": [
274
+ {
275
+ "createdBy": 137441035408492,
276
+ "creationDate": "2026-03-27 09:19:59",
277
+ "creationUserCN": "0e900d99dee8461b8",
278
+ "lastUpdatedBy": 137441035408492,
279
+ "lastUpdateDate": "2026-03-27 09:19:59",
280
+ "lastUpdateUserCN": "0e900d99dee8461b8",
281
+ "rowIdx": -1,
282
+ "id": "j738fv7ki000",
283
+ "name": "FP16",
284
+ "computingId": "i8ttm5k1tc01",
285
+ "computingName": "Hi3403V100 NNN",
286
+ "omOfflineModelUrl": null,
287
+ "omOfflineModelId": null,
288
+ "omOfflineModelSize": null,
289
+ "omOfflineModelName": null,
290
+ "omOfflineModel": [
291
+ {
292
+ "id": "1736139107008515",
293
+ "name": "yolov5s.om",
294
+ "url": null,
295
+ "size": "16172192"
296
+ },
297
+ {
298
+ "id": "1736139107008513",
299
+ "name": "reid_net.om",
300
+ "url": null,
301
+ "size": "22616799"
302
+ }
303
+ ],
304
+ "omOfflineModelLink": "",
305
+ "releaseTime": "2026-03-26",
306
+ "boardOs": "Linux",
307
+ "modelLicense": "https://gitee.com/HiSpark/modelzoo/tree/master/samples/built-in/tracking/deepsort/LICENSE",
308
+ "modelPerformance": [
309
+ {
310
+ "performanceValue": "14.186",
311
+ "unit": "耗时(ms)",
312
+ "desc": ""
313
+ },
314
+ {
315
+ "performanceValue": "70.49",
316
+ "unit": "性能(fps)",
317
+ "desc": ""
318
+ },
319
+ {
320
+ "performanceValue": "157.676",
321
+ "unit": "内存(MB)",
322
+ "desc": ""
323
+ }
324
+ ],
325
+ "deleted": 0
326
+ }
327
+ ],
328
+ "deleted": 0
329
+ }
330
+ ],
331
+ "saveType": null,
332
+ "deleteType": null,
333
+ "latest": "Y",
334
+ "deleted": 0,
335
+ "modelPhase": "released",
336
+ "remark": null,
337
+ "fileInfo": null,
338
+ "reviewType": null,
339
+ "owner": "0e900d99dee8461b8",
340
+ "ownerBy": 137441035408492,
341
+ "optional": null,
342
+ "optionalList": null,
343
+ "optionalBy": null,
344
+ "downloadNum": 82,
345
+ "collectNum": null,
346
+ "isCollect": null
347
+ }
348
+ }
reid_net.om ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ee8849d9c0fcc549beee31055728f20ab6767f47c0e1706d057ede7da653da0f
3
+ size 11310387
reid_net.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:08e9a79707f465393a730f6e3a3bc96a643e67bad3d61e99bc5225cd25785bf8
3
+ size 44662689
reid_net_om-A8W8.om ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ee8849d9c0fcc549beee31055728f20ab6767f47c0e1706d057ede7da653da0f
3
+ size 11310387
yolov5s.om ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4bc64c0bc1df3c405f183dcc69ebc2280e284721cd3043a089e79764b16425e1
3
+ size 8626175
yolov5s.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ad3b25204d6342ca94765293e27ef978c8cc8d8d300b6f004dec13bc3ea48ccf
3
+ size 28944024
yolov5s_om-A8W8.om ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4bc64c0bc1df3c405f183dcc69ebc2280e284721cd3043a089e79764b16425e1
3
+ size 8626175