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CLIP-ViT Models for Open Vocabulary Image Classification

This document provides comprehensive information about CLIP-ViT models for open vocabulary image classification, including model implementation details, usage instructions, and experimental results. The CLIPVisionModelPool class manages collections of pre-trained and fine-tuned CLIP Vision models for open vocabulary image classification tasks.

The Eight Tasks

The most common eight tasks used in the research community are SUN397, Cars, RESISC45, EuroSAT, SVHN, GTSRB, MNIST, and DTD. These tasks cover a wide range of domains, including natural images, satellite images, and digit recognition. You can download the datasets from this HuggingFace Collection or using the datasets library as follows:

from datasets import load_dataset

# take `gtsrb` as an example
dataset = load_dataset("tanganke/gtsrb")

train_dataset = dataset["train"]
test_dataset = dataset["test"]

The authors of Task Arithmetic have fine-tuned the CLIP-ViT models from the open_clip library on these eight tasks and provide the models publicly on Google Drive. However, these models rely on a specific version of the open_clip library.

To make experiments more convenient and avoid dependency on a specific library version, we have re-trained these models and made them publicly available on the HuggingFace Model Hub. We use the Adam Optimizer with a fixed learning rate of 1e-5 over 4000 training steps (batch_size=32). Only the vision encoder is fine-tuned, while the text encoder remains fixed to preserve the open-vocabulary property of the model.

To use these models, you can load them from the Transformers library as follows:

Direct Model Usage

Load vision backbone directly:

from transformers import CLIPVisionModel

# load the CLIP-ViT-B/32 model, take `gtsrb` as an example
vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_gtsrb')

Substitute the vision encoder of CLIP:

from transformers import CLIPProcessor, CLIPModel

# load pre-trained CLIP model
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
# substitute the vision model with the fine-tuned one
clip_model.vision_model.load_state_dict(vision_model.vision_model.state_dict())

Using CLIPVisionModelPool

For more convenient model management, use the CLIPVisionModelPool:

from fusion_bench.modelpool import CLIPVisionModelPool
from omegaconf import DictConfig

# Initialize model pool
modelpool = CLIPVisionModelPool(
    models={
        "_pretrained_": "openai/clip-vit-base-patch32",
        "gtsrb": "tanganke/clip-vit-base-patch32_gtsrb"
    },
    processor="openai/clip-vit-base-patch32"
)

# Load models through the pool
vision_model = modelpool.load_model("gtsrb")
processor = modelpool.load_processor()

# Or load complete CLIP model
clip_model = modelpool.load_clip_model("_pretrained_")

The CLIPVisionModelPool class provides a convenient way to manage multiple CLIP Vision models, and the models are automatically downloaded from the Hugging Face Model Hub when needed. Alternatively, you can configure a model pool to automatically download models from ModelScope. This is useful if you prefer to use the ModelScope platform, which is popular in China and provides access to a wide range of models.

from fusion_bench.modelpool import CLIPVisionModelPool
from omegaconf import DictConfig

# Initialize model pool
modelpool = CLIPVisionModelPool(
    models={
        "_pretrained_": "openai-mirror/clip-vit-base-patch32",
        "gtsrb": "tanganke/clip-vit-base-patch32_gtsrb"
    },
    processor="openai-mirror/clip-vit-base-patch32",
    platform="modelscope"
)

# Load models through the pool
vision_model = modelpool.load_model("gtsrb")
processor = modelpool.load_processor()

# Or load complete CLIP model
clip_model = modelpool.load_clip_model("gtsrb")

Performance of the Fine-tuned Models

evaluate the fine-tuned CLIP-ViT-B/32 models on the eight tasks:

# evaluate singlue fine-tuned models
for task in sun397 stanford-cars resisc45 eurosat svhn gtsrb mnist dtd
do
    fusion_bench method=dummy \
        modelpool=CLIPVisionModelPool/clip-vit-base-patch32_individual \
            modelpool.models._pretrained_.pretrained_model_name_or_path=tanganke/clip-vit-base-patch32_${task} \
        taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
        report_save_path="outputs/ViT-B-32/single-task/clip-vit-base-patch32_${task}.json"
done

evaluate the fine-tuned CLIP-ViT-L/14 models on the eight tasks:

# assume you have eight GPUs, and you can evaluate the models on the eight tasks in parallel
tasks=(sun397 stanford-cars resisc45 eurosat svhn gtsrb mnist dtd)
CUDA_DEVICES=(0 1 2 3 4 5 6 7)  # List of CUDA devices to use

for i in "${!CUDA_DEVICES[@]}"; do
    task=${tasks[$i]}
    CUDA_VISIBLE_DEVICES=${CUDA_DEVICES[$i]} fusion_bench method=dummy \
        modelpool=CLIPVisionModelPool/clip-vit-large-patch14_individual \
            modelpool.models._pretrained_.pretrained_model_name_or_path=tanganke/clip-vit-large-patch14_${task} \
        taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
            taskpool.clip_model=openai/clip-vit-large-patch14 \
        report_save_path="outputs/ViT-L-14/single-task/clip-vit-large-patch14_${task}.json" &
done
Model SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
Pre-trained 63.2 59.8 60.7 46.0 31.6 32.5 48.3 43.9 48.2
SUN397 75.0 47.0 54.3 46.5 28.3 26.4 44.3 41.6 45.4
Cars 56.6 78.3 50.9 38.4 30.2 30.6 49.7 41.8 47.1
RESISC45 52.0 47.2 95.2 56.9 23.9 24.3 39.7 35.9 46.9
EuroSAT 49.0 39.9 33.5 99.0 11.8 22.9 33.8 35.5 40.7
SVHN 40.5 36.3 18.9 9.8 97.3 27.3 81.8 23.2 41.9
GTSRB 36.8 33.0 20.6 21.3 41.2 98.9 30.9 23.9 38.3
MNIST 50.3 40.0 31.3 17.7 50.1 19.3 99.6 30.7 42.4
DTD 54.6 51.3 36.9 25.0 28.9 21.8 47.3 79.7 43.2
Model SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
SUN397 78.9 56.2 58.9 46.6 42.7 39.9 59.3 40.8 52.9
Cars 62.2 85.9 60.8 48.7 47.1 44.8 61.6 43.2 56.8
RESISC45 60.5 57.8 96.6 65.7 28.4 35.6 71.5 39.0 56.9
EuroSAT 58.3 59.2 37.4 99.0 40.5 38.9 57.4 37.7 53.6
SVHN 57.6 55.4 42.8 19.6 97.6 32.6 90.0 33.1 53.6
GTSRB 54.0 50.5 25.3 13.2 52.0 99.0 56.9 33.9 48.1
MNIST 58.7 52.4 47.0 23.6 65.0 27.6 99.7 37.7 51.5
DTD 57.7 58.1 53.5 43.0 44.2 36.2 70.4 82.3 55.7
Model SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
Pre-trained 68.3 77.8 71.0 58.9 58.4 50.6 76.4 55.5 64.6
SUN397 82.8 68.4 58.1 49.9 55.0 46.3 79.5 52.8 61.6
Cars 67.8 92.9 68.7 56.4 51.7 47.7 80.5 55.6 65.2
RESISC45 65.6 69.0 97.4 64.3 38.3 46.6 77.7 49.9 63.6
EuroSAT 65.2 69.0 40.6 99.2 33.4 45.6 73.5 47.1 59.2
SVHN 66.4 69.0 54.0 19.7 97.9 48.7 92.2 50.1 62.3
GTSRB 63.4 64.8 38.7 19.6 71.0 99.2 75.1 45.8 59.7
MNIST 56.0 49.8 53.5 26.6 48.2 33.1 99.8 47.1 51.7
DTD 66.8 75.3 65.5 43.7 49.5 45.0 68.5 85.5 62.5

CLIPVisionModelPool Implementation

The CLIPVisionModelPool class extends the base BaseModelPool class and provides specialized functionality for managing CLIP Vision models from Hugging Face Transformers library.

Key Features

  • Multi-platform Support: Supports both Hugging Face (hf) and ModelScope (modelscope) platforms.
  • Model Loading: Handles CLIPVisionModel and CLIPModel loading with automatic path resolution
  • Dataset Integration: Built-in support for loading train/validation/test datasets via the datasets library.
  • Processor Management: Manages CLIPProcessor instances for consistent image preprocessing.
  • Model Persistence: Save and load models with proper state preservation.

Class Configuration

The CLIPVisionModelPool accepts the following parameters:

  • models (DictConfig): Configuration mapping model names to their paths or configurations
  • processor (Optional[DictConfig]): Configuration for the CLIP processor (defaults to corresponding CLIP model processor)
  • platform (Literal["hf", "huggingface", "modelscope"]): Platform for model/dataset loading (default: "hf")
  • train_datasets, val_datasets, test_datasets (Optional[DictConfig]): Dataset configurations

Model Pool Configuration

To use these models from our FusionBench library, you can specify the modelpool configuration file as follows:

config/modelpool/CLIPVisionModelPool/clip-vit-base-patch32_TA8.yaml
# eight image classification tasks defined in task arithmetic paper
defaults:
  - /dataset/image_classification/train@train_datasets:
      - sun397
      - stanford-cars
      - resisc45
      - eurosat
      - svhn
      - gtsrb
      - mnist
      - dtd
  - /dataset/image_classification/test@test_datasets:
      - sun397
      - stanford-cars
      - resisc45
      - eurosat
      - svhn
      - gtsrb
      - mnist
      - dtd
  - _self_
_target_: fusion_bench.modelpool.CLIPVisionModelPool
_recursive_: False
processor: openai/clip-vit-base-patch32
models:
  _pretrained_: openai/clip-vit-base-patch32
  sun397: tanganke/clip-vit-base-patch32_sun397
  stanford-cars: tanganke/clip-vit-base-patch32_stanford-cars
  resisc45: tanganke/clip-vit-base-patch32_resisc45
  eurosat: tanganke/clip-vit-base-patch32_eurosat
  svhn: tanganke/clip-vit-base-patch32_svhn
  gtsrb: tanganke/clip-vit-base-patch32_gtsrb
  mnist: tanganke/clip-vit-base-patch32_mnist
  dtd: tanganke/clip-vit-base-patch32_dtd
platform: hf

The configuration uses YAML's inheritance feature with the defaults key. It inherits from a template (_template.yaml) and overrides specific values. Some values are set to ??? or null, indicating that they need to be specified or can be optionally set when using this configuration. This configuration structure allows for modular and reusable setups, making it easier to manage different model configurations within the FusionBench library.

config/modelpool/CLIPVisionModelPool/_template.yaml
_usage_: |
  defaults:
    - CLIPVisionModelPool@: _template
_target_: fusion_bench.modelpool.CLIPVisionModelPool
_version_: "0.2"
_recursive_: False
models: ???
train_datasets: null
test_datasets: null
processor:
  _target_: transformers.CLIPProcessor.from_pretrained
  pretrained_model_name_or_path: openai/clip-vit-base-patch32

The type of the modelpool is fusion_bench.modelpool.CLIPVisionModelPool.

Special Model Handling

The modelpool recognizes special model names:

  • _pretrained_: Reserved name for the base pretrained model
  • All other names are treated as task-specific fine-tuned models
  • Use has_pretrained property to check for pretrained model availability
  • Access model lists via all_model_names (includes special) or model_names (excludes special)

Basic Usage Example

from fusion_bench.modelpool import CLIPVisionModelPool
from omegaconf import DictConfig

# Configure the model pool
config = DictConfig({
    "models": {
        "_pretrained_": "openai/clip-vit-base-patch32",
        "sun397": "tanganke/clip-vit-base-patch32_sun397",
        "cars": "tanganke/clip-vit-base-patch32_stanford-cars"
    },
    "processor": "openai/clip-vit-base-patch32",
})

# Initialize the model pool
modelpool = CLIPVisionModelPool(config.models, processor=config.processor)

# Load models
pretrained_model = modelpool.load_model("_pretrained_")
sun397_model = modelpool.load_model("sun397")

# Load processor
processor = modelpool.load_processor()

# Load complete CLIP model
clip_model = modelpool.load_clip_model("_pretrained_")

# Check available models
print(f"All models: {modelpool.all_model_names}")
print(f"Task-specific models: {modelpool.model_names}")
print(f"Has pretrained model: {modelpool.has_pretrained}")

Advanced Configuration Example

from fusion_bench.modelpool import CLIPVisionModelPool

# Advanced configuration with custom loading parameters
modelpool = CLIPVisionModelPool(
    models={
        "_pretrained_": {
            "_target_": "transformers.CLIPVisionModel.from_pretrained",
            "pretrained_model_name_or_path": "openai/clip-vit-base-patch32",
            "torch_dtype": "auto",
            "device_map": "cuda:0"
        },
        "sun397": "tanganke/clip-vit-base-patch32_sun397"
    },
    processor={
        "_target_": "transformers.CLIPProcessor.from_pretrained", 
        "pretrained_model_name_or_path": "openai/clip-vit-base-patch32"
    },
    train_datasets={
        "sun397": "tanganke/sun397",
        "cars": "tanganke/stanford-cars"
    },
)

# Load dataset
train_dataset = modelpool.load_train_dataset("sun397")

Working with ModelScope Platform

# Use ModelScope platform (popular in China)
modelpool = CLIPVisionModelPool(
    models={
        "_pretrained_": "openai-community/clip-vit-base-patch32",
        "custom_task": "your-modelscope-username/custom-model"
    },
    platform="modelscope"
)

# Models and datasets will be loaded from ModelScope hub
model = modelpool.load_model("_pretrained_")

LoRA and L-LoRA Models

We have fine-tuned CLIP-ViT-B/16 models on the eight image classification tasks using LoRA (Low-Rank Adaptation) and L-LoRA (Linearized LoRA) methods.

Training Configuration

  • Target modules: q_proj and v_proj layers
  • Learning rate: 1e-5 using Adam optimizer
  • Training steps: 2000 steps
  • Fine-tuning script: Available at examples/clip_finetune/clip_finetune.sh

Available Model Collections

Loading LoRA Models

Load LoRA models using PEFT (see load_lora_vision_model_hf):

from transformers import CLIPVisionModel
from peft import PeftModel

# Load base model
base_model = CLIPVisionModel.from_pretrained('openai/clip-vit-base-patch16').vision_model

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, peft_model_id)

Loading L-LoRA Models

Load L-LoRA models using the specialized loader (refer to load_l_lora_vision_model_hf):

from fusion_bench.models.linearized.vision_model import load_l_lora_vision_model_hf

# Load L-LoRA model
model = load_l_lora_vision_model_hf(
    base_model_name='openai/clip-vit-base-patch16',
    lora_model_name=lora_model_id
)

Integration with CLIPVisionModelPool

The CLIPVisionModelPool can be configured to work with LoRA/L-LoRA models by specifying appropriate model configurations:

from fusion_bench.modelpool import CLIPVisionModelPool

# Configure pool for LoRA models
modelpool = CLIPVisionModelPool(
    models={
        "_pretrained_": "openai/clip-vit-base-patch16",
        "sun397_lora": {
            "_target_": "fusion_bench.models.linearized.vision_model.load_lora_vision_model_hf",
            "base_model_name": "openai/clip-vit-base-patch16",
            "lora_model_name": "tanganke/clip-vit-base-patch16_sun397_lora"
        }
    }
)
Model SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
SUN397 70.8 64.8 66.7 55.4 51.8 44.0 52.2 46.0 56.5
Cars 65.8 72.3 65.7 54.5 52.3 44.1 54.1 45.3 56.8
RESISC45 66.2 64.6 88.9 65.4 51.8 43.6 54.7 45.6 60.1
EuroSAT 65.6 64.6 59.4 97.1 48.2 43.6 60.5 46.0 60.6
SVHN 65.5 64.1 65.3 39.1 93.2 45.5 83.0 45.1 62.6
GTSRB 65.5 63.9 64.2 28.6 56.9 91.0 71.3 45.5 60.9
MNIST 65.3 64.1 65.7 51.9 57.6 46.6 98.4 45.2 61.9
DTD 64.5 64.2 61.0 49.1 54.2 44.2 68.0 67.9 59.1
Model SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
SUN397 69.0 65.0 66.7 56.0 52.6 44.0 53.3 45.4 56.5
Cars 65.8 69.7 65.7 54.4 52.0 43.7 52.6 45.3 56.2
RESISC45 65.9 64.3 83.6 66.3 51.7 43.4 51.9 45.6 59.1
EuroSAT 65.5 64.8 64.2 95.4 50.7 43.8 58.4 45.9 61.1
SVHN 65.3 64.4 65.2 46.6 90.1 45.8 80.0 45.4 62.9
GTSRB 65.5 64.4 64.2 43.8 59.5 78.6 72.6 45.2 61.7
MNIST 65.3 64.5 65.0 53.3 57.6 45.6 96.4 45.5 61.7
DTD 65.7 64.7 65.9 54.5 51.6 44.4 58.2 56.2 57.7

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Usage Examples

This section demonstrates various ways to use CLIP-ViT models for open vocabulary image classification with different fusion methods using the fusion_bench command line interface and the CLIPVisionModelPool API.

Model Information Inspection

Inspect basic information about CLIP-ViT models including parameter counts:

# Inspect CLIP-ViT-B/32 model
fusion_bench \
  method=dummy \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_individual \
  taskpool=dummy  # dummy task reports basic model information (e.g., parameter count)

# Output:
# {'model_info': {'trainable_params': 87456000, 'all_params': 87456000, 'trainable_percentage': 1.0}}

# Inspect CLIP-ViT-L/14 model
fusion_bench \
  method=dummy \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_individual \
  taskpool=dummy

# Output:
# {'model_info': {'trainable_params': 303179776, 'all_params': 303179776, 'trainable_percentage': 1.0}}

Programmatic Model Information

You can also inspect models programmatically using the CLIPVisionModelPool:

from fusion_bench.modelpool import CLIPVisionModelPool

# Initialize model pool
modelpool = CLIPVisionModelPool(
    models={"clip_model": "openai/clip-vit-base-patch32"}
)

# Load and inspect model
model = modelpool.load_model("clip_model")
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

print(f"Total parameters: {total_params:,}")
print(f"Trainable parameters: {trainable_params:,}")
print(f"Model architecture: {model}")

Single Model Evaluation

evaluate a single CLIP-ViT-B/32 model on the eight downstream tasks:

path_to_clip_model="tanganke/clip-vit-base-patch32_sun397"

fusion_bench \
  method=dummy \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_individual \
    modelpool.models._pretrained_.pretrained_model_name_or_path="'${path_to_clip_model}'" \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

Here:

  • The dummy method is a special method used to skip the model merging process, it loads the pre-trained model in the modelpool and return the model without any modification (or the first model when a model with the name _pretrained_ does not exist in modelpool), see dummy method for more information.
  • The CLIPVisionModelPool/clip-vit-base-patch32_individual modelpool contains a single model. By passing argument modelpool.models.0.path=..., we override the path of the model with the specified path.
    config/modelpool/CLIPVisionModelPool/clip-vit-base-patch32_individual.yaml
    defaults:
  - CLIPVisionModelPool@: _template
models:
  _pretrained_:
    _target_: transformers.CLIPVisionModel.from_pretrained
    pretrained_model_name_or_path: ${...base_model}
processor:
  _target_: transformers.CLIPProcessor.from_pretrained
  pretrained_model_name_or_path: ${..base_model}
base_model: openai/clip-vit-base-patch32
  • The CLIPVisionModelTaskPool/clip-vit-classification_TA8 taskpool is used to evaluate the model on the eight tasks. if $path_to_clip_model is not specified, the pre-trained model from HuggingFace will be used by default.
    config/taskpool/CLIPVisionModelTaskPool/clip-vit-classification_TA8.yaml
    defaults:
  - CLIPVisionModelTaskPool@: _template
  - /dataset/image_classification/test@test_datasets:
      - sun397
      - stanford-cars
      - resisc45
      - eurosat
      - svhn
      - gtsrb
      - mnist
      - dtd

Use a for loop to evaluate multiple CLIP-ViT-B/32 model on the eight tasks, and save reports to json files:

for task in sun397 stanford-cars resisc45 eurosat svhn gtsrb mnist dtd
do
    fusion_bench method=dummy \
        modelpool=CLIPVisionModelPool/clip-vit-base-patch32_individual \
            modelpool.models._pretrained_.pretrained_model_name_or_path=tanganke/clip-vit-base-patch32_${task} \
        taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
        report_save_path="outputs/ViT-B-32/single-task/clip-vit-base-patch32_${task}.json"
done

evaluate the CLIP-ViT-L/14 model on the eight tasks

fusion_bench method=dummy \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_individual \
    modelpool.models._pretrained_.pretrained_model_name_or_path="'${path_to_clip_model}'" \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

Simple Averaging

merge CLIP-ViT-B/32 models using simple average and evaluate on the eight tasks

fusion_bench \
  method=simple_average \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8_model_only \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 

# results
{
    "svhn": {"accuracy": 0.6451674699783325, "loss": 1.128771424293518},
    "stanford_cars": {"accuracy": 0.625668466091156, "loss": 1.135254979133606},
    "resisc45": {"accuracy": 0.7079365253448486, "loss": 0.9697789549827576},
    "eurosat": {"accuracy": 0.7685185074806213, "loss": 0.6301173567771912},
    "gtsrb": {"accuracy": 0.5494061708450317, "loss": 1.492265224456787},
    "mnist": {"accuracy": 0.8626000285148621, "loss": 0.5933865308761597},
    "dtd": {"accuracy": 0.5090425610542297, "loss": 1.79731023311615},
    "sun397": {"accuracy": 0.6543576717376709, "loss": 1.1993952989578247},
}

merge CLIP-ViT-L/14 models using simple average and evaluate on the eight tasks

fusion_bench method=simple_average \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8_model_only \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

Fisher Merging

merge CLIP-ViT-B/32 models using Fisher Merging and evaluate on the eight tasks

fusion_bench \
  method=fisher_merging/clip_fisher_merging \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

merge CLIP-ViT-L/14 models using Fisher Merging and evaluate on the eight tasks

fusion_bench \
  method=fisher_merging/clip_fisher_merging \
    method.dataloader_kwargs.batch_size=8 method.dataloader_kwargs.num_workers=4 \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

RegMean

merge CLIP-ViT-B/32 models using RegMean and evaluate on the eight tasks

fusion_bench method=regmean/clip_regmean \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

For CLIP-ViT-L/14 models:

fusion_bench \
  method=regmean/clip_regmean \
    method.dataloader_kwargs.batch_size=8 method.dataloader_kwargs.num_workers=4 \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

RegMean++

Run and evaluate the RegMean++ algorithm on eight image classification tasks:

for model in clip-vit-base-patch32 clip-vit-base-patch16 clip-vit-large-patch14
do
  fusion_bench \
      method=regmean_plusplus/clip_regmean_plusplus \
      modelpool=CLIPVisionModelPool/${model}_TA8 \
      taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
        taskpool.base_model=openai/${model} \
      report_save_path=outputs/${model}_TA8_regmean_plusplus.json
done

Task Arithmetic

merge CLIP-ViT-B/32 models using task arithmetic and evaluate on the eight tasks

fusion_bench method=task_arithmetic method.scaling_factor=0.3\
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

# results
{
    "svhn": {"accuracy": 0.77927166223526, "loss": 0.7050645351409912},
    "stanford_cars": {"accuracy": 0.5565228462219238, "loss": 1.4873239994049072},
    "resisc45": {"accuracy": 0.6487301588058472, "loss": 1.3709946870803833},
    "eurosat": {"accuracy": 0.7674074172973633, "loss": 0.6550557017326355},
    "gtsrb": {"accuracy": 0.6850356459617615, "loss": 1.2349143028259277},
    "mnist": {"accuracy": 0.9606999754905701, "loss": 0.1570172756910324},
    "dtd": {"accuracy": 0.471808522939682, "loss": 2.1495635509490967},
    "sun397": {"accuracy": 0.571083128452301, "loss": 1.7016042470932007},
}

# or use a for loop to try different scaling factors 
# and save the results to different files
for scaling_factor in 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
do
  fusion_bench \
    method=task_arithmetic method.scaling_factor=$scaling_factor \
    modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
    taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
    report_save_path=outputs/clip-vit-base-patch32_TA8_task_arithmetic_scaling_factor_${scaling_factor}.json
done

merge CLIP-ViT-L/14 models using task arithmetic and evaluate on the eight tasks

fusion_bench method=task_arithmetic method.scaling_factor=0.3\
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

Ties-Merging

merge CLIP-ViT-B/32 models using Ties-Merging and evaluate on the eight tasks

fusion_bench method=ties_merging method.scaling_factor=0.3 method.threshold=20 \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

# or use a for loop to try different scaling factors
# and save the results to different files
for scaling_factor in 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
do
  fusion_bench \
    method=ties_merging method.scaling_factor=$scaling_factor method.threshold=20 \
    modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
    taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
    report_save_path=outputs/clip-vit-base-patch32_TA8_ties_merging_scaling_factor_${scaling_factor}.json
done

merge CLIP-ViT-L/14 models using Ties-Merging and evaluate on the eight tasks

fusion_bench method=ties_merging method.scaling_factor=0.3 method.threshold=20 \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

AdaMerging

merge CLIP-ViT-B/32 models using task-wise AdaMerging and evaluate on the eight tasks, and save the merging weights by specifying the method.save_merging_weights parameter

fusion_bench \
  method=adamerging \
    method.name=clip_task_wise_adamerging \
    method.save_merging_weights=outputs/clip-vit-base-patch32_TA8_task_wise_adamerging_weights.pt \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

merge CLIP-ViT-L/14 models using task-wise AdaMerging and evaluate on the eight tasks, and save the merging weights by specifying the method.save_merging_weights parameter. Here we split the training process into two stages, the first stage is to train the merging weights, and the second stage is to evaluate the model with the learned merging weights.

# learn the merging weights.
# the per-device batch size is 4, and the total batch size is 4*4=16
fusion_bench print_config=false \
  method=adamerging \
    method.name=clip_task_wise_adamerging \
    method.save_merging_weights=outputs/clip-vit-large-patch14_TA8_task_wise_adamerging_weights.pt \
    method.devices=4 method.batch_size=4 \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=dummy # dummy taskpool is used to skip the evaluation process

# by specifying the learned merging weights, we skip the training process and directly evaluate the model
fusion_bench print_config=false \
  method=adamerging \
    method.name=clip_task_wise_adamerging \
    method.weights=outputs/clip-vit-large-patch14_TA8_task_wise_adamerging_weights.pt \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

merge CLIP-ViT-B/32 models using layer-wise AdaMerging and evaluate on the eight tasks

fusion_bench \
    method=adamerging \
        method.name=clip_layer_wise_adamerging \
        method.save_merging_weights=merging_weights.pt \
    modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
    taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
    fabric.loggers.root_dir=outputs/logs/ViT-B-32 \
    fabric.loggers.name=clip_layer_wise_adamerging_adamerging

merge CLIP-ViT-L/14 models using layer-wise AdaMerging and evaluate on the eight tasks

# learn the merging weights.
# the per-device batch size is 4, and the total batch size is 4*4=16
fusion_bench print_config=false \
  method=adamerging \
    method.name=clip_layer_wise_adamerging \
    method.save_merging_weights=outputs/clip-vit-large-patch14_TA8_layer_wise_adamerging_weights.pt \
    method.devices=4 method.batch_size=4 \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=dummy # dummy taskpool is used to skip the evaluation process

# by specifying the learned merging weights, we skip the training process and directly evaluate the model
fusion_bench \
  method=adamerging \
    method.name=clip_layer_wise_adamerging \
    method.weights=outputs/clip-vit-large-patch14_TA8_layer_wise_adamerging_weights.pt \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

Weight-Ensembling MoE

fuse CLIP-ViT-B/32 models using Weight-Ensembling Mixture of Experts and evaluate on the eight tasks

fusion_bench \
  method=weight_ensembling_moe \
    method.name=clip_weight_ensembling_moe \
    method.use_grad_accumulate=false \
    method.save_checkpoint=outputs/clip-vit-base-patch32_TA8_weight_ensembling_moe_checkpoint.ckpt \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8

fuse CLIP-ViT-L/14 models using Weight-Ensembling Mixture of Experts and evaluate on the eight tasks

# merge eight CLIP-ViT-L/14 models using WE MoE, fine-tune the routers
fusion_bench print_config=false \
  method=weight_ensembling_moe \
    method.name=clip_weight_ensembling_moe \
    method.use_grad_accumulate=true \
    method.save_checkpoint=outputs/clip-vit-large-patch14_TA8_weight_ensembling_moe_checkpoint.ckpt \
    method.batch_size=4 method.devices=4 \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=dummy &&

# load the checkpoint and evaluate the model
fusion_bench \
  method=weight_ensembling_moe \
    method.name=clip_weight_ensembling_moe \
    method.checkpoint=outputs/clip-vit-large-patch14_TA8_weight_ensembling_moe_checkpoint.ckpt \
  modelpool=CLIPVisionModelPool/clip-vit-large-patch14_TA8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8_L14

Experimental Results

We provide the experimental results of the CLIP-ViT models for open vocabulary image classification on the eight tasks in the following table.

Hyperparameters not fully optimized

The hyperparameters used in these merging methods are not fully optimized and should be considered as preliminary results only. We welcome any discoveries of more effective parameters and would be grateful for your contributions to help us improve our results.

Please note that some model merging paper results were obtained using OpenCLIP models, which may show discrepancies with the results presented here. In such cases, the results reported in the original papers should be considered authoritative.

Method SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
Reference Results
Pre-trained 63.2 59.8 60.7 46.0 31.6 32.5 48.2 43.9 48.2
Fine-tuned (STL) 75.0 78.3 95.2 99.0 97.3 98.9 99.6 79.7 90.3
Traditional MTL 72.3 76.6 92.2 97.9 95.5 97.7 99.3 77.7 88.6
Model Merging
Simple Averaging 65.4 62.6 70.8 76.9 64.5 54.9 86.3 50.9 66.5
Fisher Merging 66.7 64.0 72.2 91.6 69.0 64.3 83.5 53.7 70.6
RegMean 68.6 70.0 84.6 95.4 92.6 83.4 98.4 66.1 82.4
RegMean++ 69.3 70.5 86.7 96.1 94.1 90.4 99.0 68.7 84.4
Task Arithmetic (\(\lambda=0.3\)) 57.1 55.7 64.9 76.7 77.9 68.5 96.1 47.2 68.0
Concrete Task Arithmetic (\(\lambda=0.3\)) 64.2 63.3 75.6 94.1 90.3 82.9 98.0 52.5 77.6
Ties-Merging (\(\lambda=0.3\)) 67.1 64.2 74.1 76.8 77.7 69.4 94.1 54.0 72.2
Task-wise AdaMerging (\(\lambda=0.3\)) 58.6 56.9 69.8 82.4 70.3 58.9 97.2 55.3 68.7
Layer-wise AdaMerging (\(\lambda=0.3\)) 67.9 71.3 83.5 92.7 87.4 92.9 98.2 67.0 82.6
Concrete Layer-wise AdaMerging (\(\lambda=0.3\)) 69.1 72.7 85.9 94.7 91.3 95.7 98.7 66.8 84.4
Model Mixing
Efficient Weight-Ensembling MoE (\(90\%\)) 74.3 76.3 92.7 97.9 96.1 98.6 99.5 77.8 89.1
Weight-Ensembling MoE 73.7 76.8 93.4 98.2 96.8 98.2 99.6 76.6 89.2
Method SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
Reference Results
Pre-trained 65.5 64.6 66.3 54.1 51.9 43.4 51.7 44.9 55.3
Fine-tuned (STL) 78.9 85.9 96.6 99.0 97.6 99.0 99.7 82.3 92.3
Model Merging
Simple Averaging 68.7 69.0 75.0 83.2 74.9 62.5 93.7 51.1 72.3
Fisher Merging 70.8 71.8 76.2 93.4 77.4 61.2 90.7 52.3 74.2
RegMean 72.6 78.8 89.2 96.3 94.9 90.0 98.8 67.9 86.0
RegMean++ 72.8 78.9 89.3 97.3 96.0 93.0 99.1 71.0 87.2
Task Arithmetic (\(\lambda=0.3\)) 65.9 68.3 75.4 84.5 88.8 81.9 98.0 53.9 77.1
Ties-Merging (\(\lambda=0.3\)) 70.6 71.2 79.8 87.5 83.2 76.2 96.4 55.4 77.5
Layer-wise AdaMerging (\(\lambda=0.3\)) 70.6 79.6 86.1 93.6 93.5 95.4 98.1 62.9 85.0
Model Mixing
Efficient Weight-Ensembling MoE (\(90\%\)) 77.7 85.0 94.9 98.2 97.2 98.9 99.5 81.4 91.6
Weight-Ensembling MoE 77.2 85.0 94.8 98.3 97.3 98.9 99.6 80.8 91.5
Method SUN397 Cars RESISC45 EuroSAT SVHN GTSRB MNIST DTD Average
Reference Results
Pre-trained 68.3 77.8 71.0 58.9 58.4 50.6 76.4 55.5 64.6
Fine-tuned (STL) 82.8 92.9 97.4 99.2 97.9 99.2 99.8 85.5 94.3
Traditional MTL 79.0 89.3 94.5 98.4 96.4 98.1 99.4 83.7 92.4
Model Merging
Simple Averaging 72.5 81.5 82.2 90.0 81.6 74.0 96.6 61.8 80.0
Fisher Merging 70.6 79.4 84.1 98.1 74.7 85.0 89.5 61.0 80.3
RegMean 76.9 89.8 93.0 97.5 96.3 94.1 98.7 77.0 90.4
RegMean++ 77.2 89.6 92.8 97.5 96.9 96.3 99.2 78.4 91.0
Task Arithmetic (\(\lambda=0.3\)) 72.0 79.0 80.5 86.0 87.5 83.5 98.0 58.8 80.7
Ties-Merging (\(\lambda=0.3\)) 74.7 83.3 86.4 91.3 89.7 85.2 97.8 63.9 84.0
Task-wise AdaMerging (\(\lambda=0.3\)) 75.8 80.1 77.2 83.6 68.4 93.5 93.1 69.0 80.1
Layer-wise AdaMerging (\(\lambda=0.3\)) 78.1 90.7 90.8 96.5 94.8 97.5 98.6 81.3 91.0
Model Mixing
Efficient Weight-Ensembling MoE (\(90\%\)) 81.5 92.0 96.0 97.8 97.7 99.1 99.5 84.1 93.5
Weight-Ensembling MoE 81.5 92.3 96.5 98.8 97.6 99.4 99.6 84.5 93.8

Scope

Task Vector Cosine Similarity

Compute the cosine similarities between the task vectors and save the results to a CSV file.

# CLIP-ViT-B/32 models
fusion_bench \
  method=task_vector_cos_similarity \
    method.save_to_csv='outputs/clip-vit-base-patch32_cos.csv' \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  taskpool=dummy  # do not evaluate the model

# CLIP-ViT-L/14 models
fusion_bench \
  method=task_vector_cos_similarity \
    method.save_to_csv='outputs/clip-vit-large-patch14_cos.csv' \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  tsakpool=dummy
alt text
Cosine similarity matrices of task vectors for CLIP-ViT-B/32 and CLIP-ViT-L/14 models.

Generalization and Robustness Evaluation

You can also evaluate the generalization and robustness of different multi-task model fusion methods by change the configurations.

Instruction for running the generalization experiments:

fusion_bench \
    method=... \
    modelpool=CLIPVisionModelPool/clip-vit-base-patch32_generalization_exp1 # or `clip-vit-base-patch32_generalization_exp2`

Instruction for running the robustness experiments:

# corription can be one of the following values: 
# contrast, gaussian_noise, impulse_noise, jpeg_compression, motion_blur, pixelate, spatter
# or pass `taskpool=clip-vit-base-patch32_robustness_clean` to evaluate the model on clean data
corruption=contrast
fusion_bench \
    --config-name clip-vit-base-patch32_robustness_corrupted \
    corruption=${corruption} \
    method=... \

Below is an example of different types of corruptions:

alt text
An example of corruption data visualization, in which the corruption image generation method refers to Hendrycks & Dietterich (2019) 1.

Experimental Results

Hyperparameters not fully optimized

The hyperparameters used in these merging methods are not fully optimized and should be considered as preliminary results only. We welcome any discoveries of more effective parameters and would be grateful for your contributions to help us improve our results.

Please note that some model merging paper results were obtained using OpenCLIP models, which may show discrepancies with the results presented here. In such cases, the results reported in the original papers should be considered authoritative.

Seen Tasks Unseen Tasks
Method SUN397 Cars RESISC45 DTD SVHN GTSRB Avg. MNIST EuroSAT Avg.
Pre-trained 63.2 59.9 60.6 43.9 23.5 30.4 46.9 47.6 45.6 46.6
Fisher Merging 65.5 67.2 78.2 57.6 84.2 75.9 71.4 71.8 49.4 60.6
RegMean 69.5 70.8 88.7 67.2 95.2 89.4 80.1 82.9 44.6 63.8
RegMean++ 69.8 70.8 90.2 70.3 95.5 93.2 81.6 81.3 44.1 62.7
Task Arithmetic 64.3 63.0 73.2 54.9 84.7 79.5 69.9 75.5 42.6 59.1
Ties-Merging 68.3 65.5 76.9 54.9 75.4 72.0 68.9 73.1 47.3 60.2
Layer-wise AdaMerging 68.4 71.9 87.9 69.1 92.2 93.8 80.5 77.7 47.3 62.5
Weight-Ensembling MoE 75.4 77.5 94.3 77.0 96.8 98.7 86.6 78.3 44.0 61.1
Seen Tasks Unseen Tasks
Method SUN397 Cars GTSRB EuroSAT DTD MNIST Avg. RESISC45 SVHN Avg.
Pre-trained 63.2 59.9 30.4 45.6 43.9 47.6 48.4 60.6 23.5 40.1
Fisher Merging 68.1 67.4 67.2 86.4 58.6 81.6 71.5 60.2 42.5 51.3
RegMean 70.4 71.9 89.3 97.6 69.8 98.8 83.0 49.4 49.0 49.2
RegMean++ 70.6 71.4 94.2 96.8 70.5 99.2 83.8 50.8 54.8 52.8
Task Arithmetic 65.2 63.6 76.1 87.1 56.4 94.2 73.8 52.4 45.2 48.8
Ties-Merging 68.2 65.9 70.0 81.2 56.0 89.0 71.7 60.3 47.3 53.8
Layer-wise AdaMerging 69.8 72.4 95.5 95.1 70.7 98.1 83.6 48.7 60.7 54.7
Weight-Ensembling MoE 74.3 78.1 98.8 98.7 75.1 99.5 87.4 47.3 51.3 49.3

Table: Results of the robustness experiments (\(\lambda=0.3\)).

Method Cars EuroSAT RESISC45 GTSRB Avg. Cars EuroSAT RESISC45 GTSRB Avg.
Clean Test set Motion Blur
Fisher Merging 66.0 92.7 83.7 78.7 80.3 60.7 57.6 81.7 78.4 69.6
RegMean 73.1 97.2 91.2 95.1 89.1 70.8 71.3 88.7 87.9 79.7
RegMean++ 73.7 96.7 91.9 96.6 89.7 72.6 71.2 89.9 93.6 81.8
Task Arithmetic 64.6 91.8 80.2 74.8 77.9 62.4 59.2 78.5 63.3 65.9
Ties-Merging 65.2 83.3 78.1 67.4 73.5 64.4 53.9 76.4 57.1 62.9
Layer-wise AdaMerging 75.2 94.3 87.6 96.7 88.5 72.4 72.7 85.3 94.3 81.2
Weight-Ensembling MoE 77.4 98.9 94.4 99.0 92.4 76.5 74.2 93.7 97.4 85.5
Impulse Noise Gaussian Noise
Fisher Merging 61.5 50.0 74.7 52.6 59.7 61.6 48.1 76.0 51.3 59.3
RegMean 68.1 54.2 85.1 69.2 69.1 69.6 42.8 87.1 69.8 67.3
RegMean++ 69.3 48.6 85.8 76.2 70.0 71.4 39.9 88.2 74.2 68.4
Task Arithmetic 59.8 53.3 72.3 45.0 57.6 61.5 52.5 75.0 50.1 59.8
Ties-Merging 60.2 45.6 69.8 38.3 53.5 61.8 47.3 73.1 42.3 56.1
Layer-wise AdaMerging 69.2 40.0 79.6 83.3 68.0 70.0 53.3 82.1 80.0 71.4
Weight-Ensembling MoE 75.1 9.7 91.5 91.8 67.0 76.5 9.6 92.7 88.7 66.8
Pixelate Spatter
Fisher Merging 2.2 34.0 17.0 63.2 29.1 61.4 64.2 74.6 47.3 61.9
RegMean 2.3 38.1 17.1 90.9 37.1 68.5 64.0 84.6 83.9 75.2
RegMean++ 2.2 37.9 17.2 94.2 37.9 69.7 60.3 84.2 89.3 75.9
Task Arithmetic 2.3 33.2 19.1 65.6 30.0 61.0 62.5 72.8 57.0 63.3
Ties-Merging 3.3 31.8 18.0 58.5 27.9 61.3 52.9 70.3 48.1 58.2
Layer-wise AdaMerging 1.3 52.9 21.0 91.0 41.5 68.4 55.9 78.3 92.3 73.7
Weight-Ensembling MoE 0.5 11.6 2.3 97.5 28.0 75.1 9.7 91.4 96.3 68.1
Contrast JPEG Compression
Fisher Merging 63.8 58.4 75.5 70.4 67.0 66.3 67.6 82.6 58.9 68.8
RegMean 70.7 62.9 87.1 91.5 78.0 72.4 76.6 91.1 83.4 80.9
RegMean++ 72.1 63.0 87.7 95.5 79.6 73.5 76.3 91.8 89.6 82.8
Task Arithmetic 62.3 55.7 75.3 70.8 66.0 63.9 66.1 80.1 61.0 67.8
Ties-Merging 64.2 52.4 74.8 63.5 63.7 65.0 59.5 77.9 53.2 63.9
Layer-wise AdaMerging 73.1 67.4 83.0 96.2 79.9 72.9 70.7 86.3 90.6 80.1
Weight-Ensembling MoE 77.2 34.7 93.1 98.4 75.9 77.3 61.0 94.1 95.7 82.0

API Reference

CLIPVisionModelPool Class

clip_vision

modelpool
CLIPVisionModelPool

Bases: BaseModelPool

A model pool for managing Hugging Face's CLIP Vision models.

This class extends the base ModelPool class and overrides its methods to handle the specifics of the CLIP Vision models provided by the Hugging Face Transformers library.

Source code in fusion_bench/modelpool/clip_vision/modelpool.py 
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class CLIPVisionModelPool(BaseModelPool):
    """
    A model pool for managing Hugging Face's CLIP Vision models.

    This class extends the base `ModelPool` class and overrides its methods to handle
    the specifics of the CLIP Vision models provided by the Hugging Face Transformers library.
    """

    _config_mapping = BaseModelPool._config_mapping | {
        "_processor": "processor",
        "_platform": "hf",
    }

    def __init__(
        self,
        models: DictConfig,
        *,
        processor: Optional[DictConfig] = None,
        platform: Literal["hf", "huggingface", "modelscope"] = "hf",
        **kwargs,
    ):
        super().__init__(models, **kwargs)
        self._processor = processor
        self._platform = platform

    def load_processor(self, *args, **kwargs) -> CLIPProcessor:
        assert self._processor is not None, "Processor is not defined in the config"
        if isinstance(self._processor, str):
            if rank_zero_only.rank == 0:
                log.info(f"Loading `transformers.CLIPProcessor`: {self._processor}")
            repo_path = resolve_repo_path(
                repo_id=self._processor, repo_type="model", platform=self._platform
            )
            processor = CLIPProcessor.from_pretrained(repo_path, *args, **kwargs)
        else:
            processor = instantiate(self._processor, *args, **kwargs)
        return processor

    def load_clip_model(self, model_name: str, *args, **kwargs) -> CLIPModel:
        model_config = self._models[model_name]

        if isinstance(model_config, str):
            if rank_zero_only.rank == 0:
                log.info(f"Loading `transformers.CLIPModel`: {model_config}")
            repo_path = resolve_repo_path(
                repo_id=model_config, repo_type="model", platform=self._platform
            )
            clip_model = CLIPModel.from_pretrained(repo_path, *args, **kwargs)
            return clip_model
        else:
            assert isinstance(
                model_config, DictConfig
            ), "Model config must be a DictConfig"
            model_config = deepcopy(model_config)
            with open_dict(model_config):
                model_config._target_ = "transformers.CLIPModel.from_pretrained"
            clip_model = instantiate(model_config, *args, **kwargs)
            return clip_model

    @override
    def save_model(self, model: CLIPVisionModel, path: str):
        """
        Save a CLIP Vision model to the given path.

        Args:
            model (CLIPVisionModel): The model to save.
            path (str): The path to save the model to.
        """
        with timeit_context(f'Saving clip vision model to "{path}"'):
            model.save_pretrained(path)

    def load_model(
        self, model_name_or_config: Union[str, DictConfig], *args, **kwargs
    ) -> CLIPVisionModel:
        """
        This method is used to load a CLIPVisionModel from the model pool.

        Example configuration could be:

        ```yaml
        models:
            cifar10: tanganke/clip-vit-base-patch32_cifar10
            sun397: tanganke/clip-vit-base-patch32_sun397
            stanford-cars: tanganke/clip-vit-base-patch32_stanford-cars
        ```

        Args:
            model_name_or_config (Union[str, DictConfig]): The name of the model or the model configuration.

        Returns:
            CLIPVisionModel: The loaded CLIPVisionModel.
        """
        if (
            isinstance(model_name_or_config, str)
            and model_name_or_config in self._models
        ):
            model = self._models[model_name_or_config]
            if isinstance(model, str):
                if rank_zero_only.rank == 0:
                    log.info(f"Loading `transformers.CLIPVisionModel`: {model}")
                repo_path = resolve_repo_path(
                    model, repo_type="model", platform=self._platform
                )
                return CLIPVisionModel.from_pretrained(repo_path, *args, **kwargs)
            if isinstance(model, nn.Module):
                if rank_zero_only.rank == 0:
                    log.info(f"Returning existing model: {model}")
                return model
        else:
            # If the model is not a string, we use the default load_model method
            return super().load_model(model_name_or_config, *args, **kwargs)

    def load_train_dataset(self, dataset_name: str, *args, **kwargs):
        dataset_config = self._train_datasets[dataset_name]
        if isinstance(dataset_config, str):
            if rank_zero_only.rank == 0:
                log.info(
                    f"Loading train dataset using `datasets.load_dataset`: {dataset_config}"
                )
            dataset = self._load_dataset(dataset_config, split="train")
        else:
            dataset = super().load_train_dataset(dataset_name, *args, **kwargs)
        return dataset

    def load_val_dataset(self, dataset_name: str, *args, **kwargs):
        dataset_config = self._val_datasets[dataset_name]
        if isinstance(dataset_config, str):
            if rank_zero_only.rank == 0:
                log.info(
                    f"Loading validation dataset using `datasets.load_dataset`: {dataset_config}"
                )
            dataset = self._load_dataset(dataset_config, split="validation")
        else:
            dataset = super().load_val_dataset(dataset_name, *args, **kwargs)
        return dataset

    def load_test_dataset(self, dataset_name: str, *args, **kwargs):
        dataset_config = self._test_datasets[dataset_name]
        if isinstance(dataset_config, str):
            if rank_zero_only.rank == 0:
                log.info(
                    f"Loading test dataset using `datasets.load_dataset`: {dataset_config}"
                )
            dataset = self._load_dataset(dataset_config, split="test")
        else:
            dataset = super().load_test_dataset(dataset_name, *args, **kwargs)
        return dataset

    def _load_dataset(self, name: str, split: str):
        """
        Load a dataset by its name and split.

        Args:
            dataset_name (str): The name of the dataset.
            split (str): The split of the dataset to load (e.g., "train", "validation", "test").

        Returns:
            Dataset: The loaded dataset.
        """
        datset_dir = resolve_repo_path(
            name, repo_type="dataset", platform=self._platform
        )
        dataset = load_dataset(datset_dir, split=split)
        return dataset
load_model(model_name_or_config, *args, **kwargs)

This method is used to load a CLIPVisionModel from the model pool.

Example configuration could be:

models:
    cifar10: tanganke/clip-vit-base-patch32_cifar10
    sun397: tanganke/clip-vit-base-patch32_sun397
    stanford-cars: tanganke/clip-vit-base-patch32_stanford-cars

Parameters:

  • model_name_or_config (Union[str, DictConfig]) –

    The name of the model or the model configuration.

Returns:

  • CLIPVisionModel ( CLIPVisionModel ) –

    The loaded CLIPVisionModel.

Source code in fusion_bench/modelpool/clip_vision/modelpool.py 
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def load_model(
    self, model_name_or_config: Union[str, DictConfig], *args, **kwargs
) -> CLIPVisionModel:
    """
    This method is used to load a CLIPVisionModel from the model pool.

    Example configuration could be:

    ```yaml
    models:
        cifar10: tanganke/clip-vit-base-patch32_cifar10
        sun397: tanganke/clip-vit-base-patch32_sun397
        stanford-cars: tanganke/clip-vit-base-patch32_stanford-cars
    ```

    Args:
        model_name_or_config (Union[str, DictConfig]): The name of the model or the model configuration.

    Returns:
        CLIPVisionModel: The loaded CLIPVisionModel.
    """
    if (
        isinstance(model_name_or_config, str)
        and model_name_or_config in self._models
    ):
        model = self._models[model_name_or_config]
        if isinstance(model, str):
            if rank_zero_only.rank == 0:
                log.info(f"Loading `transformers.CLIPVisionModel`: {model}")
            repo_path = resolve_repo_path(
                model, repo_type="model", platform=self._platform
            )
            return CLIPVisionModel.from_pretrained(repo_path, *args, **kwargs)
        if isinstance(model, nn.Module):
            if rank_zero_only.rank == 0:
                log.info(f"Returning existing model: {model}")
            return model
    else:
        # If the model is not a string, we use the default load_model method
        return super().load_model(model_name_or_config, *args, **kwargs)
save_model(model, path)

Save a CLIP Vision model to the given path.

Parameters:

  • model (CLIPVisionModel) –

    The model to save.

  • path (str) –

    The path to save the model to.

Source code in fusion_bench/modelpool/clip_vision/modelpool.py 
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@override
def save_model(self, model: CLIPVisionModel, path: str):
    """
    Save a CLIP Vision model to the given path.

    Args:
        model (CLIPVisionModel): The model to save.
        path (str): The path to save the model to.
    """
    with timeit_context(f'Saving clip vision model to "{path}"'):
        model.save_pretrained(path)

Linearized Vision Models

vision_model

linearize_lora_model_(model)

Linearizes the LoraLayer modules in a PyTorch model according to the PETA paper.

Source code in fusion_bench/models/linearized/vision_model.py 
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def linearize_lora_model_(model):
    """
    Linearizes the LoraLayer modules in a PyTorch model according to the PETA paper.
    """
    for key, module in model.named_modules():
        # if isinstance(module, LoraLayer) and isinstance(module, nn.Linear):
        if isinstance(module, LoraLayer):
            # print("L-LoRA MODULE : ", module)
            parent, target, target_name = _get_submodules(model, key)
            setattr(parent, target_name, LinearizedModelWraper(target))
            # print("Linearized Lora Layer")
    return model
load_fft_vision_model_hf(model_name, return_vison_model=True)

Load a CLIP vision model from Hugging Face.

Parameters:

  • model_name
    (str) –

    The name of the CLIP vision model to load from Hugging Face.

  • return_vison_model
    (bool, default: True ) –

    If False, the full CLIPVisionModel is returned. If True, only the vision model (CLIPVisionTransformer) is returned. Defaults to True.

Returns:

  • Union[CLIPVisionTransformer, CLIPVisionModel] –

    Union[CLIPVisionTransformer, CLIPVisionModel]: The vision model.

Source code in fusion_bench/models/linearized/vision_model.py 
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def load_fft_vision_model_hf(
    model_name: str, return_vison_model=True
) -> Union[CLIPVisionTransformer, CLIPVisionModel]:
    """
    Load a CLIP vision model from Hugging Face.

    Args:
        model_name (str): The name of the CLIP vision model to load from Hugging Face.
        return_vison_model (bool, optional): If False, the full CLIPVisionModel is returned. If True, only the vision model (`CLIPVisionTransformer`) is returned. Defaults to True.

    Returns:
        Union[CLIPVisionTransformer, CLIPVisionModel]: The vision model.
    """
    model = CLIPVisionModel.from_pretrained(model_name)

    if return_vison_model:
        return CLIPVisionModel.from_pretrained(model_name).vision_model
    else:
        return model
load_l_lora_vision_model_hf(base_model_name, peft_name)

Load a linearized L-LoRA model from a base model and a Peft model (HuggingFace).

Source code in fusion_bench/models/linearized/vision_model.py 
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def load_l_lora_vision_model_hf(base_model_name: str, peft_name: str):
    """
    Load a linearized L-LoRA model from a base model and a Peft model (HuggingFace).
    """
    base_model = CLIPVisionModel.from_pretrained(base_model_name).vision_model
    peft_config = LoraConfig.from_pretrained(peft_name)
    peft_config.inference_mode = False  # This is important, make the model trainable
    model = get_peft_model(base_model, peft_config)
    linearize_lora_model_(model)
    for filename in ["linearized_adapter_model.safetensors"]:
        path = get_file_path(peft_name, filename)
        state_dict = load_file(path)
        for name, param in state_dict.items():
            model.get_parameter(name).data = param

    return model
load_lora_vision_model_hf(base_model_name, peft_name, merge_and_unload=False, return_vison_model=True)

Load a LoRA (Low-Rank Adaptation) vision model from Hugging Face.

This function loads a vision model and applies a LoRA adaptation to it. The model can be optionally merged and unloaded.

Parameters:

  • base_model_name
    (str) –

    The name of the base vision model to load from Hugging Face.

  • peft_name
    (str) –

    The name of the LoRA adaptation to apply to the base model.

  • merge_and_unload
    (bool, default: False ) –

    If True, the LoRA adaptation is merged into the base model and the LoRA layers are removed. Defaults to False.

  • return_vison_model
    (bool, default: True ) –

    If False, the full CLIPVisionModel is returned. If True, only the vision model (CLIPVisionTransformer) is returned. Defaults to True.

Returns:

  • PeftModel –

    The adapted vision model, optionally merged and unloaded.

Source code in fusion_bench/models/linearized/vision_model.py 
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def load_lora_vision_model_hf(
    base_model_name: str,
    peft_name: str,
    merge_and_unload: bool = False,
    return_vison_model=True,
):
    """
    Load a LoRA (Low-Rank Adaptation) vision model from Hugging Face.

    This function loads a vision model and applies a LoRA adaptation to it. The model can be optionally merged and unloaded.

    Parameters:
        base_model_name (str): The name of the base vision model to load from Hugging Face.
        peft_name (str): The name of the LoRA adaptation to apply to the base model.
        merge_and_unload (bool, optional): If True, the LoRA adaptation is merged into the base model and the LoRA layers are removed. Defaults to False.
        return_vison_model (bool, optional): If False, the full CLIPVisionModel is returned. If True, only the vision model (`CLIPVisionTransformer`) is returned. Defaults to True.

    Returns:
        PeftModel: The adapted vision model, optionally merged and unloaded.
    """
    model = CLIPVisionModel.from_pretrained(base_model_name)

    # Load the Peft model
    # note that we apply lora on type `CLIPVisionTransformer` instead of `CLIPVisionModel`
    vision_model = model.vision_model
    peft_model = PeftModel.from_pretrained(vision_model, peft_name, is_trainable=True)
    if merge_and_unload:
        vision_model = peft_model.merge_and_unload()
    else:
        vision_model = peft_model

    # Return the vision model
    if return_vison_model:
        return vision_model
    else:
        model.vision_model = vision_model
        return model

Model Configuration Schema

The CLIPVisionModelPool supports flexible model configurations:

# Simple string configuration
models = {
    "model_name": "huggingface/model-id"
}

# Advanced configuration with custom parameters
models = {
    "model_name": {
        "_target_": "transformers.CLIPVisionModel.from_pretrained",
        "pretrained_model_name_or_path": "huggingface/model-id",
        "torch_dtype": "auto",
        "device_map": "auto"
    }
}

# Mixed configuration
models = {
    "_pretrained_": "openai/clip-vit-base-patch32",
    "sun397": "tanganke/clip-vit-base-patch32_sun397",
    "custom_model": {
        "_target_": "some.custom.loader",
        "custom_param": "value"
    }
}

Platform Support

The modelpool supports multiple platforms for model and dataset loading:

  • Hugging Face Hub (platform="hf"): Default platform for loading models and datasets
  • ModelScope (platform="modelscope"): Alternative platform popular in China
# Hugging Face platform (default, platform="hf" or "huggingface")
modelpool = CLIPVisionModelPool(models, platform="hf")

# ModelScope platform
modelpool = CLIPVisionModelPool(models, platform="modelscope")

  1. Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations, 2019. ↩