Datasets:
Formats:
parquet
Size:
10K - 100K
ArXiv:
Tags:
personalization
instance_detection
instance_classification
instance_segmentation
instance_retrieval
License:
Update README.md
Browse files
README.md
CHANGED
@@ -59,6 +59,10 @@ The PODS dataset is new a benchmark for personalized vision tasks. It includes:
|
|
59 |
* 71-201 test images per instance with classification label annotations.
|
60 |
* 12 test images per instance (3 per split) with segmentation annotations.
|
61 |
|
|
|
|
|
|
|
|
|
62 |
Metadata is stored in two files:
|
63 |
* `pods_info.json`:
|
64 |
* `classes`: A list of class names
|
@@ -70,6 +74,7 @@ Metadata is stored in two files:
|
|
70 |
* `split`: One of `[train, test]` indicating if the image is in the train or test set for that class.
|
71 |
* `test_split`: For images in the `test` split, denotes which distribution-shift test split the image is in: One of `[in_distribution, pose, distractors, pose_and_distractors]`
|
72 |
|
|
|
73 |
## Using PODS
|
74 |
|
75 |
### Loading the dataset using HuggingFace
|
|
|
59 |
* 71-201 test images per instance with classification label annotations.
|
60 |
* 12 test images per instance (3 per split) with segmentation annotations.
|
61 |
|
62 |
+
PODS is split *class-wise* into a validation set (6 classes per semantic category) and a test set (14 classes per semantic category). All test performance reported in our paper is from the test set of classes.
|
63 |
+
|
64 |
+
*Within each class*, images are divided into a train/retrieval set (3 images) and a test/query set. The test/query set is then further divided into 4 test splits reflecting different distribution shifts.
|
65 |
+
|
66 |
Metadata is stored in two files:
|
67 |
* `pods_info.json`:
|
68 |
* `classes`: A list of class names
|
|
|
74 |
* `split`: One of `[train, test]` indicating if the image is in the train or test set for that class.
|
75 |
* `test_split`: For images in the `test` split, denotes which distribution-shift test split the image is in: One of `[in_distribution, pose, distractors, pose_and_distractors]`
|
76 |
|
77 |
+
|
78 |
## Using PODS
|
79 |
|
80 |
### Loading the dataset using HuggingFace
|