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+Evaluating Pre-trained Models on Task Datasets
+###############################################
+LAVIS provides pre-trained and finetuned model for off-the-shelf evaluation on task dataset. 
+Let's now see an example to evaluate BLIP model on the captioning task, using MSCOCO dataset.
+
+.. _prep coco:
+
+Preparing Datasets
+******************
+First, let's download the dataset. LAVIS provides `automatic downloading scripts` to help prepare 
+most of the public dataset, to download MSCOCO dataset, simply run
+
+.. code-block:: bash
+
+    cd lavis/datasets/download_scripts && python download_coco.py
+
+This will put the downloaded dataset at a default cache location ``cache`` used by LAVIS.
+
+If you want to use a different cache location, you can specify it by updating ``cache_root`` in ``lavis/configs/default.yaml``.
+
+If you have a local copy of the dataset, it is recommended to create a symlink from the cache location to the local copy, e.g.
+
+.. code-block:: bash
+
+    ln -s /path/to/local/coco cache/coco
+
+Evaluating pre-trained models
+******************************
+
+To evaluate pre-trained model, simply run
+
+.. code-block:: bash
+
+    bash run_scripts/blip/eval/eval_coco_cap.sh
+
+Or to evaluate a large model:
+
+.. code-block:: bash
+
+    bash run_scripts/blip/eval/eval_coco_cap_large.sh