diff --git a/csrc/mmdeploy/codebase/mmcls/multi_label_linear_cls.cpp b/csrc/mmdeploy/codebase/mmcls/multi_label_linear_cls.cpp
new file mode 100644
index 000000000..0b5dd0350
--- /dev/null
+++ b/csrc/mmdeploy/codebase/mmcls/multi_label_linear_cls.cpp
@@ -0,0 +1,55 @@
+// Copyright (c) OpenMMLab. All rights reserved.
+
+#include <algorithm>
+#include <numeric>
+
+#include "mmdeploy/codebase/mmcls/mmcls.h"
+#include "mmdeploy/core/tensor.h"
+#include "mmdeploy/core/utils/device_utils.h"
+#include "mmdeploy/core/utils/formatter.h"
+#include "mmdeploy/experimental/module_adapter.h"
+
+using std::vector;
+
+namespace mmdeploy::mmcls {
+
+class MultiLabelLinearClsHead : public MMClassification {
+ public:
+  explicit MultiLabelLinearClsHead(const Value& cfg) : MMClassification(cfg) {}
+  Result<Value> operator()(const Value& infer_res) {
+    MMDEPLOY_DEBUG("infer_res: {}", infer_res);
+    auto output = infer_res["output"].get<Tensor>();
+
+    if (!(output.shape().size() >= 2 && output.data_type() == DataType::kFLOAT)) {
+      MMDEPLOY_ERROR("unsupported `output` tensor, shape: {}, dtype: {}", output.shape(),
+                     (int)output.data_type());
+      return Status(eNotSupported);
+    }
+
+    auto class_num = (int)output.shape(1);
+
+    OUTCOME_TRY(auto _scores, MakeAvailableOnDevice(output, kHost, stream()));
+    OUTCOME_TRY(stream().Wait());
+
+    return GetLabels(_scores, class_num);
+  }
+
+ private:
+  Value GetLabels(const Tensor& scores, int class_num) const {
+    auto scores_data = scores.data<float>();
+    ClassifyOutput output;
+    for (int i = 0; i < class_num; ++i) {
+      auto label = ClassifyOutput::Label{i, scores_data[i]};
+      MMDEPLOY_DEBUG("label_id: {}, score: {}", label.label_id, label.score);
+      output.labels.push_back(label);
+    }
+    return to_value(std::move(output));
+  }
+
+ private:
+  static constexpr const auto kHost = Device{0};
+};
+
+REGISTER_CODEBASE_COMPONENT(MMClassification, MultiLabelLinearClsHead);
+
+}  // namespace mmdeploy::mmcls