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README.md

@@ -8,7 +8,7 @@ tags:
 datasets:
 - apple/TiC-DataComp
 ---
-# Model Card for Model ID
+# Model Card for TiC-CLIP-basic-sequential
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -53,6 +53,35 @@ The models are compatible with DataComp evaluation suite and our patched version
 The models can also be used to resume a training or as initialization for new training using OpenCLIP code.
 Please follow instructions in our [GitHub repo](https://github.com/apple/ml-tic-clip) to create the evaluation sets or follow [DataComp](https://github.com/mlfoundations/datacomp) for the standard evaluations on 38 datasets.
 
+The following snippet assumes the TiC-DataComp data has been prepared and following the instructions in the GitHub repo.
+```bash
+YEAR=2016 # There are no models before 2016 since data from 2014-2016 were compined into one year
+REPO="apple/TiC-CLIP-basic-sequential"
+huggingface-cli download $REPO checkpoints/$YEAR.pt
+
+## Train Cummulative
+pushd datacomp
+final_data_dir=$TIC_DATACOMP_Y_PATH/train/$YEAR/
+torchrun --nproc_per_node 8 --nnodes 1 \
+    train.py \
+    --scale "tic_medium" \
+    --dataset_resampled \
+    --data_dir $final_data_dir \
+    --output_dir "./results/" \
+    --exp_name "datacomp_medium-basic_cumulative" \
+    --imagenet_val  $IMAGENET_VAL_PATH  \
+    --save_frequency 1 \
+    --resume
+popd
+
+## Evaluate Model
+# Evaluate a ViT-B/16 model on TiC/Retrieval/Yearly/$YEAR and
+# TiC/DataCompNet/Yearly/$YEAR
+pushd datacomp
+python ../dataset_creation/tic-datacomp/generate_tasklist.py --yaml-path tasklist.yml --sample-eval --eval-tasks retrieval/yearly,datacompnet/yearly
+python evaluate.py --data_dir data/ --train_output_dir ./results --use_model "ViT-B-16 $YEAR.pt" --skip_hf --skip_db --skip_notification
+```
+
 ## Training Details
 
 ### Training Data