update README.md (#13)

* update README.md

* update

README.md

@@ -7,11 +7,11 @@ A collection of metrics to evaluate layout generation that can be easily used in
 
 | 📊 Metric | 🤗 Space |
 |:---------:|:---------:|
-| [![FID](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_generative_model_scores.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_generative_model_scores.yaml) | [`pytorch-layout-generation/layout-generative-model-scores`](https://huggingface.co/spaces/pytorch-layout-generation/layout-generative-model-scores) |
-| [![Max. IoU](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_maximum_iou.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_maximum_iou.yaml) | [`pytorch-layout-generation/layout-maximum-iou`](https://huggingface.co/spaces/pytorch-layout-generation/layout-maximum-iou) |
-| [![Avg. IoU](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_average_iou.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_average_iou.yaml) | [`pytorch-layout-generation/layout-average-iou`](https://huggingface.co/spaces/pytorch-layout-generation/layout-average-iou) |
-| [![Alignment](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_alignment.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_alignment.yaml) | [`pytorch-layout-generation/layout-alignment`](https://huggingface.co/spaces/pytorch-layout-generation/layout-alignment) |
-| [![Overlap](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_overlap.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_overlap.yaml) | [`pytorch-layout-generation/layout-overlap`](https://huggingface.co/spaces/pytorch-layout-generation/layout-overlap) |
+| [![FID](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_generative_model_scores.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_generative_model_scores.yaml) | [`creative-graphic-design/layout-generative-model-scores`](https://huggingface.co/spaces/creative-graphic-design/layout-generative-model-scores) |
+| [![Max. IoU](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_maximum_iou.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_maximum_iou.yaml) | [`creative-graphic-design/layout-maximum-iou`](https://huggingface.co/spaces/creative-graphic-design/layout-maximum-iou) |
+| [![Avg. IoU](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_average_iou.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_average_iou.yaml) | [`creative-graphic-design/layout-average-iou`](https://huggingface.co/spaces/creative-graphic-design/layout-average-iou) |
+| [![Alignment](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_alignment.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_alignment.yaml) | [`creative-graphic-design/layout-alignment`](https://huggingface.co/spaces/creative-graphic-design/layout-alignment) |
+| [![Overlap](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_overlap.yaml/badge.svg)](https://github.com/shunk031/huggingface-evaluate_layout-metrics/actions/workflows/layout_overlap.yaml) | [`creative-graphic-design/layout-overlap`](https://huggingface.co/spaces/creative-graphic-design/layout-overlap) |
 
 # How to use
 
@@ -27,13 +27,14 @@ pip install evaluate
 import evaluate
 import numpy as np
 
-# Load the evaluation metric named "pytorch-layout-generation/layout-alignment"
-alignment_score = evaluate.load("pytorch-layout-generation/layout-alignment")
+# Load the evaluation metric named "creative-graphic-design/layout-alignment"
+alignment_score = evaluate.load("creative-graphic-design/layout-alignment")
 
 # `batch_bbox` is a tensor representing (batch_size, max_num_elements, coordinates) 
-# and `batch_mask` is a tensor representing (batch_size, max_num_elements).
+# and `batch_mask` is a boolean tensor representing (batch_size, max_num_elements).
 batch_bbox = np.random.rand(512, 25, 4)
-batch_mask = np.random.rand(512, 25)
+# Note that padded fields will be set to `False`
+batch_mask = np.full((512, 25), fill_value=True)
 
 # Add the batch of bboxes and masks to the metric
 alignment_score.add_batch(batch_bbox=batch_bbox, batch_mask=batch_mask)

layout_alignment/layout-alignment.py

@@ -20,15 +20,15 @@
 Examples:
 
     Example 1: Single processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-alignment")
+        >>> metric = evaluate.load("creative-graphic-design/layout-alignment")
         >>> model_max_length, num_coordinates = 25, 4
         >>> bbox = np.random.rand(model_max_length, num_coordinates)
         >>> mask = np.random.choice(a=[True, False], size=(model_max_length,))
         >>> metric.add(bbox=bbox, mask=mask)
         >>> print(metric.compute())
 
     Example 2: Batch processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-alignment")
+        >>> metric = evaluate.load("creative-graphic-design/layout-alignment")
         >>> batch_size, model_max_length, num_coordinates = 512, 25, 4
         >>> batch_bbox = np.random.rand(batch_size, model_max_length, num_coordinates)
         >>> batch_mask = np.random.choice(a=[True, False], size=(batch_size, model_max_length))

layout_average_iou/layout-average-iou.py

@@ -19,7 +19,7 @@
 Examples:
 
     Example 1: Single processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-average-iou")
+        >>> metric = evaluate.load("creative-graphic-design/layout-average-iou")
         >>> num_samples, num_categories = 24, 4
         >>> layout = {
         >>>     "bboxes": np.random.rand(num_samples, num_categories),
@@ -29,7 +29,7 @@
         >>> print(metric.compute())
     
     Example 2: Batch processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-average-iou")
+        >>> metric = evaluate.load("creative-graphic-design/layout-average-iou")
         >>> batch_size, num_samples, num_categories = 512, 24, 4
         >>> layouts = [
         >>>     {

layout_generative_model_scores/layout-generative-model-scores.py

@@ -22,15 +22,15 @@
 Examples:
 
     Example 1: Single processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-generative-model-scores")
+        >>> metric = evaluate.load("creative-graphic-design/layout-generative-model-scores")
         >> feat_size = 256
         >>> feats_real = np.random.rand(feat_size)
         >>> feats_fake = np.random.rand(feat_size)
         >>> metric.add(feats_real=feats_real, feats_fake=feats_fake)
         >>> print(metric.compute())
     
     Example 2: Batch processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-generative-model-scores")
+        >>> metric = evaluate.load("creative-graphic-design/layout-generative-model-scores")
         >>> batch_size, feat_size = 512, 256
         >>> feats_real = np.random.rand(batch_size, feat_size)
         >>> feats_fake = np.random.rand(batch_size, feat_size)

layout_maximum_iou/layout-maximum-iou.py

@@ -29,7 +29,7 @@ class Layout(TypedDict):
 Examples:
 
     Example 1: Single processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-maximum-iou")
+        >>> metric = evaluate.load("creative-graphic-design/layout-maximum-iou")
         >>> num_samples, num_categories = 24, 4
         >>> layout1 = {
         >>>     "bboxes": np.random.rand(num_samples, num_categories),
@@ -43,7 +43,7 @@ class Layout(TypedDict):
         >>> print(metric.compute())
 
     Example 2: Batch processing
-        >>> metric = evaluate.load("pytorch-layout-generation/layout-maximum-iou")
+        >>> metric = evaluate.load("creative-graphic-design/layout-maximum-iou")
         >>> batch_size, num_samples, num_categories = 512, 24, 4
         >>> layouts1 = [
         >>>     {