Update arg links with new path

ankandrew · Apr 12, 2024 · e3ba3e1 · e3ba3e1
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Showing 4 changed files with 16 additions and 16 deletions.
diff --git a/README.md b/README.md
@@ -31,9 +31,9 @@ The idea is to use this after a plate object detector, since the OCR expects the
 
 ### Available Models
 
-|          Model Name          | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                      Dataset                                                      | Accuracy<sup>[2]</sup> |              Dataset              |
-|:----------------------------:|:--------------------------------:|:----------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------:|:---------------------------------:|
-| argentinian-plates-cnn-model |              2.0964              |                      477                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_plate_dataset.zip) |         94.05%         | Non-synthetic, plates up to 2020. |
+|          Model Name          | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                         Dataset                                                         | Accuracy<sup>[2]</sup> |              Dataset              |
+|:----------------------------:|:--------------------------------:|:----------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------:|:----------------------:|:---------------------------------:|
+| argentinian-plates-cnn-model |              2.0964              |                      477                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip) |         94.05%         | Non-synthetic, plates up to 2020. |
 
 _<sup>[1]</sup> Inference on Mac M1 chip using CPUExecutionProvider. Utilizing CoreMLExecutionProvider accelerates speed
 by 5x._
@@ -62,9 +62,9 @@ _<sup>[2]</sup> Accuracy is what we refer as plate_acc. See metrics section._
 
   ```shell
   pip install fast-plate-ocr[train]
-  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_cnn_ocr_config.yaml
-  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_cnn_ocr.keras
-  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_plate_benchmark.zip
+  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_cnn_ocr_config.yaml
+  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_cnn_ocr.keras
+  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_benchmark.zip
   unzip arg_plate_benchmark.zip
   fast_plate_ocr valid \
       -m arg_cnn_ocr.keras \
@@ -157,7 +157,7 @@ To train the model you will need:
     img_width: 140
     ```
 2. A labeled dataset,
-   see [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_plate_dataset.zip)
+   see [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip)
    for the expected data format.
 3. Run train script:
     ```shell

diff --git a/docs/index.md b/docs/index.md
@@ -20,9 +20,9 @@ The idea is to use this after a plate object detector, since the OCR expects the
 
 We currently have the following available models:
 
-|           Model Name           | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                      Dataset                                                      | Accuracy<sup>[2]</sup> |              Dataset              |
-|:------------------------------:|:--------------------------------:|:----------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------:|:---------------------------------:|
-| `argentinian-plates-cnn-model` |              2.0964              |                      477                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_plate_dataset.zip) |         94.05%         | Non-synthetic, plates up to 2020. |
+|           Model Name           | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                         Dataset                                                         | Accuracy<sup>[2]</sup> |              Dataset              |
+|:------------------------------:|:--------------------------------:|:----------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------:|:----------------------:|:---------------------------------:|
+| `argentinian-plates-cnn-model` |              2.0964              |                      477                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip) |         94.05%         | Non-synthetic, plates up to 2020. |
 
 _<sup>[1]</sup> Inference on Mac M1 chip using CPUExecutionProvider. Utilizing CoreMLExecutionProvider accelerates speed
 by 5x._
@@ -51,9 +51,9 @@ Calculate Model accuracy:
 
   ```shell
   pip install fast-plate-ocr[train]
-  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_cnn_ocr_config.yaml
-  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_cnn_ocr.keras
-  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_plate_benchmark.zip
+  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_cnn_ocr_config.yaml
+  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_cnn_ocr.keras
+  curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_benchmark.zip
   unzip arg_plate_benchmark.zip
   fast_plate_ocr valid \
       -m arg_cnn_ocr.keras \

diff --git a/docs/usage.md b/docs/usage.md
@@ -71,7 +71,7 @@ To train the model you will need:
     img_width: 140
     ```
 2. A labeled dataset,
-   see [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/v1.0/arg_plate_dataset.zip)
+   see [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip)
    for the expected data format.
 3. Run train script:
     ```shell

diff --git a/fast_plate_ocr/inference/hub.py b/fast_plate_ocr/inference/hub.py
@@ -16,8 +16,8 @@
 
 AVAILABLE_ONNX_MODELS: dict[str, tuple[str, str]] = {
     "argentinian-plates-cnn-model": (
-        f"{BASE_URL}/v1.0/arg_cnn_ocr.onnx",
-        f"{BASE_URL}/v1.0/arg_cnn_ocr_config.yaml",
+        f"{BASE_URL}/arg-plates/arg_cnn_ocr.onnx",
+        f"{BASE_URL}/arg-plates/arg_cnn_ocr_config.yaml",
     )
 }
 """Available ONNX models for doing inference."""