@@ -2,7 +2,14 @@ import numpy as np
 import unittest
 
 from super_gradients.training.transforms.transforms import DetectionTargetsFormatTransform
-from super_gradients.training.utils.detection_utils import DetectionTargetsFormat
+
+from super_gradients.training.datasets.data_formats.default_formats import (
+    XYXY_LABEL,
+    LABEL_XYXY,
+    LABEL_CXCYWH,
+    LABEL_NORMALIZED_XYXY,
+    LABEL_NORMALIZED_CXCYWH,
+)
 
 
 class DetectionTargetsTransformTest(unittest.TestCase):
@@ -12,110 +19,115 @@ class DetectionTargetsTransformTest(unittest.TestCase):
     def test_label_first_2_label_last(self):
         input = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
         output = np.array([[50, 10, 20, 30, 40]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.XYXY_LABEL,
-                                                    output_format=DetectionTargetsFormat.LABEL_XYXY)
         sample = {"image": self.image, "target": input}
-        self.assertTrue(np.array_equal(transform(sample)["target"], output))
+
+        transform = DetectionTargetsFormatTransform(image_shape=self.image.shape[1:], max_targets=1, input_format=XYXY_LABEL, output_format=LABEL_XYXY)
+        t_output = transform(sample)["target"]
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_xyxy_2_normalized_xyxy(self):
         input = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
         _, h, w = self.image.shape
         output = np.array([[10, 20 / w, 30 / h, 40 / w, 50 / h]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_XYXY,
-                                                    output_format=DetectionTargetsFormat.LABEL_NORMALIZED_XYXY)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_XYXY, output_format=LABEL_NORMALIZED_XYXY
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.array_equal(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_xyxy_2_cxcywh(self):
         input = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
         _, h, w = self.image.shape
         output = np.array([[10, 30, 40, 20, 20]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_XYXY,
-                                                    output_format=DetectionTargetsFormat.LABEL_CXCYWH)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_XYXY, output_format=LABEL_CXCYWH)
         t_output = transform(sample)["target"]
-        self.assertTrue(np.array_equal(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_xyxy_2_normalized_cxcywh(self):
         input = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
         _, h, w = self.image.shape
         output = np.array([[10, 30 / w, 40 / h, 20 / w, 20 / h]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_XYXY,
-                                                    output_format=DetectionTargetsFormat.LABEL_NORMALIZED_CXCYWH)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_XYXY, output_format=LABEL_NORMALIZED_CXCYWH
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.array_equal(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_normalized_xyxy_2_cxcywh(self):
         _, h, w = self.image.shape
         input = np.array([[10, 20 / w, 30 / h, 40 / w, 50 / h]], dtype=np.float32)
         output = np.array([[10, 30, 40, 20, 20]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_NORMALIZED_XYXY,
-                                                    output_format=DetectionTargetsFormat.LABEL_CXCYWH)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_NORMALIZED_XYXY, output_format=LABEL_CXCYWH
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.allclose(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_normalized_xyxy_2_normalized_cxcywh(self):
         _, h, w = self.image.shape
         input = np.array([[10, 20 / w, 30 / h, 40 / w, 50 / h]], dtype=np.float32)
         output = np.array([[10, 30 / w, 40 / h, 20 / w, 20 / h]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_NORMALIZED_XYXY,
-                                                    output_format=DetectionTargetsFormat.LABEL_NORMALIZED_CXCYWH)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_NORMALIZED_XYXY, output_format=LABEL_NORMALIZED_CXCYWH
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.allclose(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_cxcywh_2_xyxy(self):
         output = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
         input = np.array([[10, 30, 40, 20, 20]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_CXCYWH,
-                                                    output_format=DetectionTargetsFormat.LABEL_XYXY)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_CXCYWH, output_format=LABEL_XYXY)
         t_output = transform(sample)["target"]
-        self.assertTrue(np.array_equal(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_cxcywh_2_normalized_xyxy(self):
         _, h, w = self.image.shape
         output = np.array([[10, 20 / w, 30 / h, 40 / w, 50 / h]], dtype=np.float32)
         input = np.array([[10, 30, 40, 20, 20]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_CXCYWH,
-                                                    output_format=DetectionTargetsFormat.LABEL_NORMALIZED_XYXY)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_CXCYWH, output_format=LABEL_NORMALIZED_XYXY
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.array_equal(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_normalized_cxcywh_2_xyxy(self):
         _, h, w = self.image.shape
         input = np.array([[10, 30 / w, 40 / h, 20 / w, 20 / h]], dtype=np.float32)
         output = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_NORMALIZED_CXCYWH,
-                                                    output_format=DetectionTargetsFormat.LABEL_XYXY)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_NORMALIZED_CXCYWH, output_format=LABEL_XYXY
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.allclose(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
     def test_normalized_cxcywh_2_normalized_xyxy(self):
         _, h, w = self.image.shape
         output = np.array([[10, 20 / w, 30 / h, 40 / w, 50 / h]], dtype=np.float32)
         input = np.array([[10, 30 / w, 40 / h, 20 / w, 20 / h]], dtype=np.float32)
-        transform = DetectionTargetsFormatTransform(max_targets=1,
-                                                    input_format=DetectionTargetsFormat.LABEL_NORMALIZED_CXCYWH,
-                                                    output_format=DetectionTargetsFormat.LABEL_NORMALIZED_XYXY)
         sample = {"image": self.image, "target": input}
+
+        transform = DetectionTargetsFormatTransform(
+            image_shape=self.image.shape[1:], max_targets=1, input_format=LABEL_NORMALIZED_CXCYWH, output_format=LABEL_NORMALIZED_XYXY
+        )
         t_output = transform(sample)["target"]
-        self.assertTrue(np.allclose(output, t_output))
+        self.assertTrue(np.allclose(output, t_output, atol=1e-6))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()