Oml zoo

fastembed/image/image_embedding.py

@@ -65,8 +65,8 @@ def __init__(
                 return
 
         raise ValueError(
-            f"Model {model_name} is not supported in TextEmbedding."
-            "Please check the supported models using `TextEmbedding.list_supported_models()`"
+            f"Model {model_name} is not supported in ImageEmbedding."
+            "Please check the supported models using `ImageEmbedding.list_supported_models()`"
         )
 
     def embed(

fastembed/image/onnx_embedding.py

@@ -29,9 +29,28 @@
         },
         "model_file": "model.onnx",
     },
+    {
+        "model": "Qdrant/Unicom-ViT-B-16",
+        "dim": 768,
+        "description": "Unicom Unicom-ViT-B-16 from open-metric-learning",
+        "size_in_GB": 0.82,
+        "sources": {
+            "hf": "Qdrant/Unicom-ViT-B-16",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "Qdrant/Unicom-ViT-B-32",
+        "dim": 512,
+        "description": "Unicom Unicom-ViT-B-32 from open-metric-learning",
+        "size_in_GB": 0.48,
+        "sources": {
+            "hf": "Qdrant/Unicom-ViT-B-32",
+        },
+        "model_file": "model.onnx",
+    },
 ]
 
-
 class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
     def __init__(
         self,
@@ -122,10 +141,16 @@ def _preprocess_onnx_input(
 
         return onnx_input
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
+    def _post_process_onnx_output(
+        self, output: OnnxOutputContext
+    ) -> Iterable[np.ndarray]:
         return normalize(output.model_output).astype(np.float32)
 
 
 class OnnxImageEmbeddingWorker(ImageEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> OnnxImageEmbedding:
-        return OnnxImageEmbedding(model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs)
+    def init_embedding(
+        self, model_name: str, cache_dir: str, **kwargs
+    ) -> OnnxImageEmbedding:
+        return OnnxImageEmbedding(
+            model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs
+        )

tests/test_image_onnx_embeddings.py

@@ -11,6 +11,14 @@
     "Qdrant/resnet50-onnx": np.array(
         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01046245, 0.01171397, 0.00705971, 0.0]
     ),
+    "Qdrant/Unicom-ViT-B-16": np.array(
+        [ 0.0170, -0.0361,  0.0125, -0.0428, -0.0232,  0.0232, -0.0602, -0.0333,
+         0.0155,  0.0497]
+    ),
+    "Qdrant/Unicom-ViT-B-32": np.array(
+        [0.0418,  0.0550,  0.0003,  0.0253, -0.0185,  0.0016, -0.0368, -0.0402,
+        -0.0891, -0.0186]
+    ),
 }