Update README.md

README.md

@@ -16,18 +16,18 @@ tags:
 
 This custom TST-ProcTHOR dataset is used in research work "Test-Space Training: Self-Supervised Specialization of Vision Models to the Test Environment". 
 
-- `procthor_pretraing/` is a multimodal pretraining dataset collected using ProcTHOR environment. It contains RGB images, and 9 additional tokenized modalities. 
+- `pretrain/` is a multimodal pretraining dataset collected using ProcTHOR environment. It contains RGB images, and 9 additional tokenized modalities. 
 
-- `procthor_transfer_downstream.tar.gz` is the associated downstream dataset used to finetune TST pretrained models on semantic segmentation tasks. 
+- `segmentation/train` is the associated downstream dataset used to finetune TST pretrained models on semantic segmentation tasks. 
 
-- `procthor_test_downstream.tar.gz` contains the test dataset used for evaluation/testing on semantic segmentation task. This data corresponds to samples obtained from the test-space itself.
+- `segmentation/test` contains the test dataset used for evaluation/testing on semantic segmentation task. This data corresponds to samples obtained from the test-space itself.
 
 
-## Dataset Structure Pretraining Data
+## Dataset Structure
 
 ```python
 TST-ProcTHOR/
-├── procthor_pretrain/
+├── pretrain/
 │   ├── test_spaces/
 │   │   ├── crop_settings/               # Contains .tar shards
 │   │   ├── det/                         # Contains .tar shards
@@ -42,19 +42,11 @@ TST-ProcTHOR/
 │       ├── tok_canny_edge@224/          # Contains .tar shards
 │       ├── ...                          # More tokenized feature directories
 │       └── tok_semseg@224/              # Contains .tar shards
-├── procthor_transfer_downstream.tar.gz
-└── procthor_test_downstream.tar.gz
-
+├── segmentation/
+│   ├── train/                          # Training data for segmentation
+│   └── test/                           # Test data for segmentation
+└── README.md
 ```
-### Data Splits
-
-The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews. 
-
-| Dataset Split | Number of samples | 
-|---------------|-------------------| 
-| Pretraining   | -                 |
-| Transfer      | 20000             |
-| Test          | 5000              |
 
 ## Dataset Creation