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+# Conditional Prompt Learning for Vision-Language Models (Co-CoOp, CVPR'22)
+[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2203.05557)
+
+We provide the scripts in [scripts/cocoop](../scripts/cocoop) to reproduce Co-CoOp results (CVPR'22).
+
+Make sure to configure the dataset paths in environment variable `DATA` and run the commands from the main directory `MaPLe/`.
+
+## Generalization From Base to New Classes
+
+This corresponds to the experiments in Section 4.1, i.e., Table 1.
+
+You will need both `scripts/cocoop/base2new_train.sh` and `scripts/cocoop/base2new_test.sh`. The former trains a model on bash classes while the latter evaluates the trained model on new classes. Both scripts have two input arguments, i.e., `DATASET` and `SEED`.
+
+`DATASET` takes as input a dataset name, like `imagenet` or `caltech101`. The valid names are the files' names in `CoOp/configs/datasets/`.
+
+Below we provide an example on how to evaluate the model on ImageNet.
+
+```bash
+# seed=1
+bash scripts/cocoop/base2new_train.sh imagenet 1
+bash scripts/cocoop/base2new_test.sh imagenet 1
+
+# seed=2
+bash scripts/cocoop/base2new_train.sh imagenet 2
+bash scripts/cocoop/base2new_test.sh imagenet 2
+
+# seed=3
+bash scripts/cocoop/base2new_train.sh imagenet 3
+bash scripts/cocoop/base2new_test.sh imagenet 3
+```
+
+When the evaluation is done, you can use `parse_test_res.py` to automatically calculate the average results. For instance, after you finish the evaluation (including `base2new_train.sh` and `base2new_test.sh`) on ImageNet using the aforementioned commands, you would get
+
+```
+output
+|–– base2new/
+|   |–– test_new/
+|   |   |–– imagenet/
+|   |   |   |–– shots_16/
+|   |   |   |   |–– CoCoOp/
+|   |   |   |   |   |–– vit_b16_c4_ep10_batch1_ctxv1/
+|   |   |   |   |   |   |–– seed1/
+|   |   |   |   |   |   |–– seed2/
+|   |   |   |   |   |   |–– seed3/
+|   |–– train_base/
+|   |   |–– imagenet/
+|   |   |   |–– shots_16/
+|   |   |   |   |–– CoCoOp/
+|   |   |   |   |   |–– vit_b16_c4_ep10_batch1_ctxv1/
+|   |   |   |   |   |   |–– seed1/
+|   |   |   |   |   |   |–– seed2/
+|   |   |   |   |   |   |–– seed3/
+```
+
+Then, to get the average performance on the base classes, run
+
+```bash
+python parse_test_res.py output/base2new/train_base/imagenet/shots_16/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1
+```
+
+To get the average performance on the new classes, run
+
+```bash
+python parse_test_res.py output/base2new/test_new/imagenet/shots_16/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1 --test-log
+```
+
+## Cross-Dataset Transfer
+
+This corresponds to the experiments in Section 4.2, i.e., Table 2.
+
+The relevant scripts are `scripts/cocoop/xd_train.sh` and `scripts/cocoop/xd_test.sh` where the `DATASET` variable is set to the default, namely `imagenet`. To train the model, run
+
+```bash
+# seed=1
+bash scripts/cocoop/xd_train.sh 1
+
+# seed=2
+bash scripts/cocoop/xd_train.sh 2
+
+# seed=3
+bash scripts/cocoop/xd_train.sh 3
+```
+
+Then, you evaluate the model on other datasets, e.g.,
+
+```bash
+for SEED in 1 2 3
+do
+    bash scripts/cocoop/xd_test.sh caltech101 ${SEED}
+    bash scripts/cocoop/xd_test.sh oxford_pets ${SEED}
+    bash scripts/cocoop/xd_test.sh stanford_cars ${SEED}
+done
+```
+
+## Domain Generalization
+
+This corresponds to the experiments in Section 4.3, i.e., Table 3.
+
+The steps are similar to those discussed in "Cross-Dataset Transfer" except you evaluate the model on the variants of ImageNet, i.e., `imagenetv2`, `imagenet_sketch`, `imagenet_a` and `imagenet_r`.