diff --git a/README.md b/README.md
index c91e101..1ab9319 100644
--- a/README.md
+++ b/README.md
@@ -14,13 +14,13 @@ Axis network cameras can be used for computer vision applications and can run ma
 | ---------- | ---------- | :----------: | :----------: | :----------: |
 | ARTPEC-7 (Q1615 Mk III) | [MobilenetV2](https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_quant_edgetpu.tflite) ([ckpt](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz)) | 1 | <!--A7_tf1_mnv2--> 4.55 ms <!--end_A7_tf1_mnv2--> | Top 1: 68.9% <br/> Top 5: 88.2% |
 | ARTPEC-7 (Q1615 Mk III) | [MobilenetV2](https://raw.githubusercontent.com/google-coral/test_data/master/tf2_mobilenet_v2_1.0_224_ptq_edgetpu.tflite) | 2 | <!--A7_tf2_mnv2--> 4.58 ms <!--end_A7_tf2_mnv2--> | Top 1: 69.6% <br/> Top 5: 89.1%  |
-| ARTPEC-7 (Q1615 Mk III) | [MobilenetV3](https://raw.githubusercontent.com/google-coral/test_data/master/tf2_mobilenet_v3_edgetpu_1.0_224_ptq_edgetpu.tflite) | 2 | <!--A7_tf2_mnv3--> 4.70 ms <!--end_A7_tf2_mnv3--> | Top 1: 72.7% <br/> Top 5: 91.1% |
-| ARTPEC-8 (P1465-LE) | [MobilenetV2](https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_quant.tflite) ([ckpt](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz)) | 1  | <!--A8_P_tf1_mnv2--> 9.48 ms <!--end_A8_P_tf1_mnv2--> | Top 1: 68.8% <br/> Top 5: 88.9% |
+| ARTPEC-7 (Q1615 Mk III) | [MobilenetV3](https://raw.githubusercontent.com/google-coral/test_data/master/tf2_mobilenet_v3_edgetpu_1.0_224_ptq_edgetpu.tflite) | 2 | <!--A7_tf2_mnv3--> 4.68 ms <!--end_A7_tf2_mnv3--> | Top 1: 72.7% <br/> Top 5: 91.1% |
+| ARTPEC-8 (P1465-LE) | [MobilenetV2](https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_quant.tflite) ([ckpt](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz)) | 1  | <!--A8_P_tf1_mnv2--> 9.96 ms <!--end_A8_P_tf1_mnv2--> | Top 1: 68.8% <br/> Top 5: 88.9% |
 | ARTPEC-8 (Q1656-LE)  | [MobilenetV2](https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_quant.tflite) ([ckpt](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz)) | 1  | <!--A8_tf1_mnv2--> 5.19 ms <!--end_A8_tf1_mnv2--> | Top 1: 68.8% <br/> Top 5: 88.9% |
 | CV25 (M3085-V) | [MobilenetV2](https://acap-ml-model-storage.s3.amazonaws.com/mobilenetv2_cavalry.bin) ([ckpt](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz)) | 1  | <!--cv25_tf1_mnv2--> 5.59 ms <!--end_cv25_tf1_mnv2--> | Top 1: 66.7% <br/> Top 5: 87.1% |
-| CV25 (M3085-V) | [EfficientNet-Lite0](https://acap-ml-model-storage.s3.amazonaws.com/EfficientNet-lite0.bin) ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/lite/efficientnet-lite0.tar.gz)) | 1  | <!--cv25_tf1_ens--> 7.00 ms <!--end_cv25_tf1_ens--> | Top 1: 71.2% <br/> Top 5: 90.3% |
+| CV25 (M3085-V) | [EfficientNet-Lite0](https://acap-ml-model-storage.s3.amazonaws.com/EfficientNet-lite0.bin) ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/lite/efficientnet-lite0.tar.gz)) | 1  | <!--cv25_tf1_ens--> 7.01 ms <!--end_cv25_tf1_ens--> | Top 1: 71.2% <br/> Top 5: 90.3% |
 
-*Values for AXIS OS 11.2.*
+*Values for AXIS OS 11.3.*
 
 ## How are the measures calculated?