Zero-Shot Image Classification
TiC-CLIP
vision
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@@ -55,12 +55,14 @@ The models can also be used to resume a training or as initialization for new tr
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  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.
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  The following snippet assumes the TiC-DataComp data has been prepared and following the instructions in the GitHub repo.
 
 
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  ```bash
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  YEAR=2016 # There are no models before 2016 since data from 2014-2016 were compined into one year
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  REPO="apple/TiC-CLIP-basic-sequential"
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  huggingface-cli download $REPO checkpoints/$YEAR.pt
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- ## Train Cummulative
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  pushd datacomp
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  final_data_dir=$TIC_DATACOMP_Y_PATH/train/$YEAR/
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  torchrun --nproc_per_node 8 --nnodes 1 \
@@ -74,7 +76,10 @@ torchrun --nproc_per_node 8 --nnodes 1 \
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  --save_frequency 1 \
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  --resume
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  popd
 
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  ## Evaluate Model
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  # Evaluate a ViT-B/16 model on TiC/Retrieval/Yearly/$YEAR and
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  # TiC/DataCompNet/Yearly/$YEAR
@@ -83,6 +88,28 @@ python ../dataset_creation/tic-datacomp/generate_tasklist.py --yaml-path tasklis
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  python evaluate.py --data_dir data/ --train_output_dir ./results --use_model "ViT-B-16 $YEAR.pt" --skip_hf --skip_db --skip_notification
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  ```
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  ## Training Details
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  ### Training Data
 
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  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.
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  The following snippet assumes the TiC-DataComp data has been prepared and following the instructions in the GitHub repo.
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+
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+ ### Training
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  ```bash
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  YEAR=2016 # There are no models before 2016 since data from 2014-2016 were compined into one year
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  REPO="apple/TiC-CLIP-basic-sequential"
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  huggingface-cli download $REPO checkpoints/$YEAR.pt
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+ ## Train
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  pushd datacomp
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  final_data_dir=$TIC_DATACOMP_Y_PATH/train/$YEAR/
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  torchrun --nproc_per_node 8 --nnodes 1 \
 
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  --save_frequency 1 \
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  --resume
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  popd
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+ ```
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+ ### Evaluation
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+ ```bash
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  ## Evaluate Model
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  # Evaluate a ViT-B/16 model on TiC/Retrieval/Yearly/$YEAR and
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  # TiC/DataCompNet/Yearly/$YEAR
 
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  python evaluate.py --data_dir data/ --train_output_dir ./results --use_model "ViT-B-16 $YEAR.pt" --skip_hf --skip_db --skip_notification
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  ```
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+ ### OpenCLIP Load and Inference Example
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+ ```python
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+ import open_clip
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+ from huggingface_hub import hf_hub_download
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+ filename = hf_hub_download(repo_id="apple/TiC-CLIP-basic-sequential", filename="checkpoints/2016.pt")
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+ model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', filename)
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+ tokenizer = open_clip.get_tokenizer('ViT-B-16')
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+
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+ image = preprocess(Image.open("image.png").convert('RGB')).unsqueeze(0)
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+ text = tokenizer(["a diagram", "a dog", "a cat"])
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+
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+ with torch.no_grad(), torch.cuda.amp.autocast():
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+ image_features = model.encode_image(image)
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+ text_features = model.encode_text(text)
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+ image_features /= image_features.norm(dim=-1, keepdim=True)
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+ text_features /= text_features.norm(dim=-1, keepdim=True)
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+
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+ text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
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+
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+ print("Label probs:", text_probs)
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+ ```
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+
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  ## Training Details
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  ### Training Data