Update README.md

README.md

@@ -1,14 +1,16 @@
----
-license: apache-2.0
-language:
-- en
-pipeline_tag: zero-shot-image-classification
-tags:
-- point-cloud
-- contrastive-learning
-- multi-modal
-- clip
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: zero-shot-image-classification
+tags:
+- point-cloud
+- contrastive-learning
+- multi-modal
+- clip
+datasets:
+- Ximeng0831/CTP-Dataset
+---
 
 # CTP: Contrastive Tensor Pre-training
 [![arXiv](https://img.shields.io/badge/arXiv-2603.07874-b31b1b.svg)](https://arxiv.org/abs/2603.07874)
@@ -36,36 +38,23 @@ The checkpoints are organized by experiment configuration. We use the following
 | `192_cos_matrix_pc` | Pairwise (Frozen) | 3× Pairwise Similarity Matrices |
 | `192_cos_matrix_IP_pc`| Image-Point Only | 1× Similarity Matrix (I-L) |
 
-## How to Load the Models
+## Download the Checkpoints
 
-You can load these checkpoints directly into your PyTorch environment using the `huggingface_hub` library.
+You can download pretrained checkpoints using the `huggingface_hub` library:
 
-### Prerequisites
-```bash
-pip install torch huggingface_hub
-```
-
-### Loading a Specific Checkpoint
 ```python
-import torch
 from huggingface_hub import hf_hub_download
 
-REPO_ID = "Ximeng0831/CTP"
-# Example: Loading the default proposed model
-SUBFOLDER = "pointnet2/192_l2_cube_all"
-FILENAME = "ckpt_epoch9.pt"
+# Available: ["192_l2_tensor_all", "192_l2_tensor_nm_all", "192_cos_tensor_all", "192_cos_matrix_all", "192_l2_tensor_pc", "192_cos_matrix_pc", "192_cos_matrix_IP_pc"]
+
+config_name = "192_l2_tensor_all"
 
 checkpoint_path = hf_hub_download(
-    repo_id=REPO_ID,
-    subfolder=SUBFOLDER,
-    filename=FILENAME
+    repo_id="Ximeng0831/CTP",
+    subfolder=config_name,
+    filename="ckpt_epoch9.pt",
+    # local_dir="checkpoints"
 )
-
-# Load CTP model
-# model = ctp(text_encoder, image_encoder, lidar_encoder, loss_fn)
-checkpoint = torch.load(checkpoint_path, map_location="cpu")
-model.load_state_dict(checkpoint)
-model.eval()
 ```
 Source code: https://github.com/TAMU-CVRL/CTP