edited README.md

README.md

@@ -139,9 +139,36 @@ cd transformer_benchmark
 python train.py --config Configs/conditional_detr.yaml
 ```
 
-### Identity Classification
+### Temporal Classification
 - Use `tracklet_id` (1-8) from the PKL file as labels.  
-- **Temporal Split**: 30% train / 30% val / 40% test (chronological order).  
+- **Temporal Split**: 30% train / 30% val / 40% test (chronological order). 
+
+### Benchmark vision models for temporal classification:
+
+Step 1: cropping the bounding boxes from `pmfeed_4_3_16.mp4` using the correct labels in `pmfeed_4_3_16_bboxes_and_labels.pkl`. Then convert the folder of images cropped from `pmfeed_4_3_16.mp4` into lmdb dataset for fast loading:
+```
+cd identification_benchmark
+python crop_pmfeed_4_3_16.py
+python construct_lmdb.py
+```
+
+Step 2: get embeddings from vision model:
+```
+cd big_model_inference
+```
+Use `inference_resnet.py` to get embeddings from resnet and `inference_transformers.py` to get embeddings from transformer weights available on Huggingface:
+```
+python inference_resnet.py --resnet_type resnet18
+python inference_transformers.py --model_name facebook/convnextv2-nano-1k-224
+```
+
+Step 3: use the embeddings and labels obtained from step 2 to conduct knn evaluation and linear classification:
+
+```
+cd ../classification
+python train.py
+python knn_evaluation.py
+```
 
 ## Key Results