Update README.md

README.md

@@ -1,36 +1,17 @@
-# DATASET
-
-All ShapeNet https://drive.google.com/file/d/1gwRviPb18vANDECAqeUqLBCvBTnfq7fo/view [30GB] 
-ShapeNet 3 Classes https://drive.google.com/drive/folders/10dVJxcSIjp3EfQkqEiA2xJ9N9mtyTQFV?usp=drive_link
-
-Chairs Airplane Cars
-
-[ OOD --> Toy4k -> ModelNet ] 
-
-## Evaluate on AWS
-
-```ssh -i "3dv.pem" [email protected]```
-
-```conda activate diffusion-classifier```
-
-```cd /home/ubuntu/3D-Building-Classification-main/diffusion_classifier```
-
-```python eval_prob_adaptive.py --dataset shapenet --split test --n_trials 1 --to_keep 3 1 --n_samples 100 500 --loss l1 --prompt_path prompts/shapenet_prompts_meher.csv```
-
-Then the log directory will be something like this: ``` data/shapenet/v2-0_1trials_3_1keep_100_500samples_l1 ```
+# Multi-Modal Diffusion Models For 3D Objects Classification 🏢🌐
 
-Accuracy can be computed by running: ``` python scripts/print_acc.py data/shapenet/v2-0_1trials_3_1keep_100_500samples_l1 ```
+Team: Biruk Abere, Berfin Inal, Gabriele Dominici, Meher Nigam, Alex Li,  Nursena Koprucu, Sharvaree Vadgama, Le Xue, Shicheng Xu, Alberto Tono 
 
-Use pytorch 11.7
+![Diagram (1)](https://github.com/user-attachments/assets/55b38187-cf11-436a-8fc5-4fa1e31e7908)
 
 
-# Multi-Modal Diffusion Models For 3D Objects Classification 🏢🌐
+This repository contains the implementation of our approach that integrates the methodologies of the LION paper and the Diffusion Classifier for advanced 3D building classification. 
 
-Team: Biruk Abere, Berfin Inal, Gabriele Dominici, Meher Nigam, Alex Li,  Nursena Koprucu, Sharvaree Vadgama, Le Xue, Shicheng Xu, Alberto Tono 
+# DATASET
 
-This repository contains the implementation of our approach that integrates the methodologies of the LION paper and the Diffusion Classifier for advanced 3D building classification. 
+ShapeNet first 200 shapes in chairs and cars
 
-![3D Building Classification]()
+![NewHero1](https://github.com/user-attachments/assets/b7be2bde-1339-4205-90ee-65137b962dce)
 
 
 
@@ -40,7 +21,13 @@ This repository contains the implementation of our approach that integrates the
   * cd multi-modal-diffusion-models
   * pip install -r requirements.txt
 
-# Results  📈
+# LION (CD3DO)
+  * for the LION use ```pytorch 11.7```
+  * ```conda activate diffusion-classifier```
+  * ```cd /home/ubuntu/3D-Building-Classification-main/diffusion_classifier```
+  * ```python eval_prob_adaptive.py --dataset shapenet --split test --n_trials 1 --to_keep 3 1 --n_samples 100 500 --loss l1 --prompt_path prompts/shapenet_prompts_meher.csv```
+  * Then the log directory will be something like this: ``` data/shapenet/v2-0_1trials_3_1keep_100_500samples_l1 ```
+  * Accuracy can be computed by running: ``` python scripts/print_acc.py data/shapenet/v2-0_1trials_3_1keep_100_500samples_l1 ```
 
 
 # Licence 📜