Add EdgeCrafter Model

application/backend/app/execution/common/geti_config_converter.py

@@ -777,6 +777,26 @@ def convert(config: dict) -> dict:
                 "status": ModelStatus.ACTIVE,
                 "default": False,
             },
+            "object-detection-edgecrafter-s": {
+                "recipe_path": RECIPE_PATH / "detection" / "edgecrafter_s.yaml",
+                "status": ModelStatus.ACTIVE,
+                "default": False,
+            },
+            "object-detection-edgecrafter-m": {
+                "recipe_path": RECIPE_PATH / "detection" / "edgecrafter_m.yaml",
+                "status": ModelStatus.ACTIVE,
+                "default": False,
+            },
+            "object-detection-edgecrafter-l": {
+                "recipe_path": RECIPE_PATH / "detection" / "edgecrafter_l.yaml",
+                "status": ModelStatus.ACTIVE,
+                "default": False,
+            },
+            "object-detection-edgecrafter-x": {
+                "recipe_path": RECIPE_PATH / "detection" / "edgecrafter_x.yaml",
+                "status": ModelStatus.ACTIVE,
+                "default": False,
+            },
             "object-detection-dinov3-detr-s": {
                 "recipe_path": RECIPE_PATH / "detection" / "deimv2_s.yaml",
                 "status": ModelStatus.ACTIVE,

application/backend/app/supported_models/manifests/detection/edgecrafter_l.yaml

@@ -0,0 +1,42 @@
+id: object-detection-edgecrafter-l
+name: ECDet-L
+
+pretrained_weights:
+  url: https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_l.pth
+  mirror_url: https://huggingface.co/Intellindust/ECDet_L/resolve/main/model.safetensors
+  sha_sum: 60e48c51b33d70acf8e3a3faddbdb70b0876c64509b74cb623106cc7c31e85a1
+
+description: "EdgeCrafter detection model (ECDet-L) combines an efficient ECViT backbone with a hybrid encoder and transformer decoder for real-time, high-accuracy object detection with low latency."
+
+stats:
+  gigaflops: 101.0
+  trainable_parameters: 31.0
+  benchmark_metrics:
+    coco_map_50_95: 57.0
+
+capabilities:
+  tiling: false
+
+hyperparameters:
+  training:
+    learning_rate: 0.0005
+    weight_decay: 0.0001
+    batch_size: 4
+    accumulate_grad_batches: 8
+    early_stopping:
+      patience: 10
+    scheduler:
+      factor: 0.5
+      patience: 10
+    gradient_clip:
+      enable: true
+      max_grad_norm: 0.1
+    allowed_values_input_size:
+      - 640
+    input_size_width: 640
+    input_size_height: 640
+  dataset_preparation:
+    augmentation:
+      deim_framework: true
+      random_zoom_out:
+        enable: true

application/backend/app/supported_models/manifests/detection/edgecrafter_m.yaml

@@ -0,0 +1,42 @@
+id: object-detection-edgecrafter-m
+name: ECDet-M
+
+pretrained_weights:
+  url: https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_m.pth
+  mirror_url: https://huggingface.co/Intellindust/ECDet_M/resolve/main/model.safetensors
+  sha_sum: c4cdf8bcd3b27c7903e422acd03caf733b5e1bfd664550bce43e50e2a3bbdc6e
+
+description: "EdgeCrafter detection model (ECDet-M) combines an efficient ECViT backbone with a hybrid encoder and transformer decoder for real-time, high-accuracy object detection with low latency."
+
+stats:
+  gigaflops: 53.0
+  trainable_parameters: 18.0
+  benchmark_metrics:
+    coco_map_50_95: 54.3
+
+capabilities:
+  tiling: false
+
+hyperparameters:
+  training:
+    learning_rate: 0.0005
+    weight_decay: 0.0001
+    batch_size: 8
+    accumulate_grad_batches: 4
+    early_stopping:
+      patience: 10
+    scheduler:
+      factor: 0.5
+      patience: 10
+    gradient_clip:
+      enable: true
+      max_grad_norm: 0.1
+    allowed_values_input_size:
+      - 640
+    input_size_width: 640
+    input_size_height: 640
+  dataset_preparation:
+    augmentation:
+      deim_framework: true
+      random_zoom_out:
+        enable: true

application/backend/app/supported_models/manifests/detection/edgecrafter_s.yaml

@@ -0,0 +1,43 @@
+id: object-detection-edgecrafter-s
+name: ECDet-S
+
+pretrained_weights:
+  url: https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_s.pth
+  mirror_url: https://huggingface.co/Intellindust/ECDet_S/resolve/main/model.safetensors
+  sha_sum: 35340a45101d24a094becf8776aeaa327c9e6b37453e5e81dfd2923c134c4619
+
+description: "EdgeCrafter detection model (ECDet-S) combines an efficient ECViT backbone with a hybrid encoder and transformer decoder for real-time, high-accuracy object detection with low latency."
+
+stats:
+  gigaflops: 26.0
+  trainable_parameters: 10.0
+  benchmark_metrics:
+    coco_map_50_95: 51.7
+
+capabilities:
+  tiling: false
+
+hyperparameters:
+  training:
+    learning_rate: 0.0005
+    weight_decay: 0.0001
+    batch_size: 8
+    accumulate_grad_batches: 4
+    early_stopping:
+      patience: 10
+    scheduler:
+      factor: 0.5
+      patience: 10
+    gradient_clip:
+      enable: true
+      max_grad_norm: 0.1
+    allowed_values_input_size:
+      - 640
+    input_size_width: 640
+    input_size_height: 640
+  dataset_preparation:
+    augmentation:
+      deim_framework: true
+      random_zoom_out:
+        enable: true
+        fill: 0

application/backend/app/supported_models/manifests/detection/edgecrafter_x.yaml

@@ -0,0 +1,42 @@
+id: object-detection-edgecrafter-x
+name: ECDet-X
+
+pretrained_weights:
+  url: https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_x.pth
+  mirror_url: https://huggingface.co/Intellindust/ECDet_X/resolve/main/model.safetensors
+  sha_sum: c87394be021b573fd8e25969b85a8670127120c9d0209ac7807fbadae1a5cec6
+
+description: "EdgeCrafter detection model (ECDet-X) combines an efficient ECViT backbone with a hybrid encoder and transformer decoder for real-time, high-accuracy object detection with low latency."
+
+stats:
+  gigaflops: 151.0
+  trainable_parameters: 49.0
+  benchmark_metrics:
+    coco_map_50_95: 57.9
+
+capabilities:
+  tiling: false
+
+hyperparameters:
+  training:
+    learning_rate: 0.0005
+    weight_decay: 0.0001
+    batch_size: 4
+    accumulate_grad_batches: 8
+    early_stopping:
+      patience: 10
+    scheduler:
+      factor: 0.5
+      patience: 10
+    gradient_clip:
+      enable: true
+      max_grad_norm: 0.1
+    allowed_values_input_size:
+      - 640
+    input_size_width: 640
+    input_size_height: 640
+  dataset_preparation:
+    augmentation:
+      deim_framework: true
+      random_zoom_out:
+        enable: true

application/backend/tests/unit/api/routers/test_model_architectures.py

@@ -17,7 +17,7 @@ def test_get_all_model_architectures(self, fxt_client: TestClient):
 
         data = response.json()
         assert "model_architectures" in data
-        assert len(data["model_architectures"]) == 32
+        assert len(data["model_architectures"]) == 36
 
         # Verify structure of first detection model
         detection_model = next(
@@ -57,7 +57,7 @@ def test_get_all_model_architectures(self, fxt_client: TestClient):
     @pytest.mark.parametrize(
         "task_filter, total_models",
         [
-            ("detection", 32),
+            ("detection", 36),
             ("instance_segmentation", 20),
             ("classification", 6),
         ],

library/benchmark_manifest.yaml

@@ -166,6 +166,18 @@ experiments:
       - name: yolo26_m
         priority: extended
         recipe: detection/yolo26_m.yaml
+      - name: edgecrafter_s
+        priority: extended
+        recipe: detection/edgecrafter_s.yaml
+      - name: edgecrafter_m
+        priority: extended
+        recipe: detection/edgecrafter_m.yaml
+      - name: edgecrafter_l
+        priority: extended
+        recipe: detection/edgecrafter_l.yaml
+      - name: edgecrafter_x
+        priority: extended
+        recipe: detection/edgecrafter_x.yaml
       - name: yolo26_l
         priority: extended
         recipe: detection/yolo26_l.yaml