CLIP

Contrastive Language-Image Pre-training model for zero-shot and fine-tuned image classification, image-text retrieval, and embedding extraction. Fine-tuning adapts CLIP's shared image-text embedding space to domain-specific image-caption data.

No default NGC pretrained checkpoint is required. When

train.pretrained_model_path

evaluate.checkpoint

inference.checkpoint

, or

export.checkpoint

is unset, TAO loads pretrained weights from HuggingFace for SigLIP2/OpenCLIP variants or

torch.hub

for Radio-CLIP, so first use needs network access or a local mirror.

Supported actions:

train

evaluate

inference

export

gen_trt_engine

Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read

references/skill_info.yaml

and resolve the run override from either an explicit

automl_policy

value or the user's workflow request. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as

automl_policy: off

for this run only; otherwise default to

auto

. When

automl_policy: auto

automl_enabled: true

, and both

schemas/train.schema.json

and

references/spec_template_train.yaml

are packaged, route the train action through

tao-skill-bank:tao-run-automl

by default with this model's

skill_dir

. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and

automl_policy

. Use direct model training only when

automl_policy: off

or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-train actions such as

evaluate

inference

export

, and deploy flows stay in this model skill. The per-run

automl_policy

override does not change model metadata.

Instructions

Use this skill for NVIDIA TAO CLIP jobs: training, evaluation, embedding inference, ONNX export, and TensorRT engine generation. Start by identifying the requested action, then load only the referenced files needed for that action:

defaults.json

for default parameters,

config.json

for action/data-source wiring,

references/spec_template.yaml

for full spec shape, and

references/model_info.yaml

for SDK metadata.

For dataset-backed actions, collect the required image, caption, list, or prompt files from the user and place the resolved paths in

spec_overrides

. For

export

and

gen_trt_engine

, infer parent artifacts from the upstream job when available; otherwise require explicit checkpoint, ONNX, or engine paths. Run

gen_trt_engine

, TensorRT

evaluate

, and TensorRT

inference

in the TAO Deploy image.

For TAO Deploy TensorRT actions (

gen_trt_engine

, TensorRT

evaluate

, and TensorRT

inference

), read

references/tao-deploy-clip.md

first. Deploy spec templates live in this skill's

references/

folder with the

spec_template_deploy_*.yaml

prefix.

Training Requirements

Dataset type: image_text
Formats: custom image/caption folders or WebDataset shards
Monitoring metric: val/t2i_mAP

Supported Models

SigLIP2:

siglip2-so400m-patch16-256

(default),

siglip2-so400m-patch14-224

siglip2-so400m-patch14-384

siglip2-so400m-patch16-384

siglip2-so400m-patch16-512

siglip2-so400m-patch16-naflex

Radio-CLIP:

c-radio_v3-b

c-radio_v3-l

c-radio_v3-h

c-radio_v3-g

OpenCLIP / NV-CLIP:

ViT-L-14-SigLIP-CLIPA-224

ViT-L-14-SigLIP-CLIPA-336

ViT-H-14-SigLIP-CLIPA-224

ViT-H-14-SigLIP-CLIPA-336

ViT-H-14-SigLIP-CLIPA-574

Radio-CLIP requires

model.adaptor_name

to be set to

siglip

clip

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
train	dataset.train.datasets	train_datasets	image_dir: images.tar.gz, image_list_file: image_list.txt, caption_dir: captions.tar.gz	Yes
train	dataset.train.wds.root_dir	train_wds_dataset	root directory containing `.tar` shards	No
train	dataset.train.wds.shard_list_file	train_wds_dataset	shards.txt listing shard paths	No
train	dataset.val.datasets	eval_dataset	image_dir: images.tar.gz, image_list_file: image_list.txt, caption_dir: captions.tar.gz	Yes
evaluate	dataset.val.datasets	eval_dataset	image_dir: images.tar.gz, image_list_file: image_list.txt, caption_dir: captions.tar.gz	Yes
inference	inference.datasets	inference_dataset	image_dir: images.tar.gz	Yes
inference	inference.text_file	inference_dataset	prompts.txt	No
export	export.checkpoint	parent train job or explicit checkpoint	checkpoint .pth, optional for pretrained export	No
gen_trt_engine	gen_trt_engine.onnx_file	parent export job or explicit ONNX	clip_model.onnx	No

For custom training, set

dataset.train.type: custom

and provide

dataset.train.datasets

entries. Image and caption files must share the same base name.

caption_file_suffix

defaults to

.txt

, and

image_list_file

is optional.

For WDS training, set

dataset.train.type: wds

and provide at least one of

dataset.train.wds.root_dir

dataset.train.wds.shard_list_file

root_dir

is scanned recursively for

.tar

shards.

shard_list_file

is a text file with one shard path per line; relative lines resolve under the list-file directory unless

root_dir

is also supplied, in which case they resolve under

root_dir

. Validation/evaluation data remains custom format via

dataset.val.datasets

Typical Spec Overrides

Data source overrides are mandatory for dataset-backed actions. Construct paths from the Per-Action Dataset Requirements table and include them in

spec_overrides

. For inference, provide at least one of

inference.datasets

inference.text_file

python

S3_TRAIN = "s3://bucket/data/train"
S3_WDS = "s3://bucket/data/wds"
S3_EVAL = "s3://bucket/data/eval"
S3_INFER = "s3://bucket/data/infer"

train, custom dataset:

python

{
    "train.num_epochs": 10,
    "dataset.train.type": "custom",
    "dataset.train.datasets": [{"image_dir": f"{S3_TRAIN}/images.tar.gz", "image_list_file": f"{S3_TRAIN}/image_list.txt", "caption_dir": f"{S3_TRAIN}/captions.tar.gz"}],
    "dataset.val.datasets": [{"image_dir": f"{S3_EVAL}/images.tar.gz", "image_list_file": f"{S3_EVAL}/image_list.txt", "caption_dir": f"{S3_EVAL}/captions.tar.gz"}],
}

train, WDS dataset:

python

{
    "train.num_epochs": 10,
    "dataset.train.type": "wds",
    "dataset.train.wds.root_dir": f"{S3_WDS}",
    "dataset.train.wds.shard_list_file": f"{S3_WDS}/shards.txt",
    "dataset.train.wds.samples_per_shard": 10000,
    "dataset.val.datasets": [{"image_dir": f"{S3_EVAL}/images.tar.gz", "image_list_file": f"{S3_EVAL}/image_list.txt", "caption_dir": f"{S3_EVAL}/captions.tar.gz"}],
}

evaluate:

python

{
    "dataset.val.datasets": [{"image_dir": f"{S3_EVAL}/images.tar.gz", "image_list_file": f"{S3_EVAL}/image_list.txt", "caption_dir": f"{S3_EVAL}/captions.tar.gz"}],
}

Leave

evaluate.checkpoint

unset for zero-shot evaluation with pretrained weights. Set

evaluate.trt_engine

instead of

evaluate.checkpoint

for TensorRT evaluation.

inference:

python

{
    "inference.datasets": [{"image_dir": f"{S3_INFER}/images.tar.gz"}],
    "inference.text_file": f"{S3_INFER}/prompts.txt",
}

Inference writes

image_embeddings.h5

and/or

text_embeddings.h5

under

results_dir

. The saved embeddings are L2-normalized.

export:

python

{
    "export.onnx_file": "${results_dir}/export/clip_model.onnx",
    "export.encoder_type": "combined",
    "export.batch_size": -1,
}

Set

export.encoder_type: separate

when deployment should use independent vision and text encoders. Separate export writes

_vision.onnx

and

_text.onnx

variants derived from the base

export.onnx_file

gen_trt_engine:

python

{
    "gen_trt_engine.onnx_file": "${results_dir}/export/clip_model.onnx",
    "gen_trt_engine.trt_engine": "${results_dir}/deploy/clip_model.engine",
    "gen_trt_engine.batch_size": -1,
    "gen_trt_engine.tensorrt.data_type": "fp16",
    "gen_trt_engine.tensorrt.min_batch_size": 1,
    "gen_trt_engine.tensorrt.opt_batch_size": 1,
    "gen_trt_engine.tensorrt.max_batch_size": 16,
}

Eval Dataset

Optional for training. If provided, validation metrics are computed at validation intervals. Required for

evaluate

Deploy Workflow

The skill exposes

gen_trt_engine

as the deploy action. In generated SDK runners, use

model_info["actions"]["gen_trt_engine"]

and run it in the TAO Deploy image, not the PyTorch training image. The in-container command is

clip gen_trt_engine -e {config_path}

; direct TAO Launcher usage spells the same action as

tao deploy clip gen_trt_engine -e /path/to/spec.yaml

TAO Deploy supports both combined and separate encoder formats. For separate encoders, pass the base path without

_vision

_text

gen_trt_engine.onnx_file

and

gen_trt_engine.trt_engine

; TAO detects or writes the suffixed vision/text files.

Use

evaluate.trt_engine

for TensorRT evaluation and

inference.trt_engine

for TensorRT embedding extraction. These TensorRT paths also run in the TAO Deploy image. Direct TAO Launcher usage spells these as

tao deploy clip evaluate

and

tao deploy clip inference

Full TAO Deploy reference: tao-deploy-clip.

Important Parameters

model.type: Backbone family and resolution. Use fixed-resolution SigLIP2/OpenCLIP variants for deployment.
model.adaptor_name: Required for Radio-CLIP. Set to
```
siglip
```
or
```
clip
```
.
model.image_size: Training transform image resolution. Keep it aligned with the selected fixed-resolution backbone.
train.num_epochs: CLIP fine-tuning often converges quickly. Start with 10-20 epochs for domain adaptation, then increase only if validation loss is still improving.
train.optim.vision_lr / train.optim.text_lr: Learning rates for the two encoders. CLIP is sensitive to high learning rates; reduce both if loss is unstable.
model.freeze_vision_encoder / model.freeze_text_encoder: Defaults are false. Freezing one encoder can help when the dataset is small or only one modality needs adaptation.
train.loss_type:
```
siglip
```
is recommended for SigLIP2 and Radio-CLIP. Use
```
clip
```
for CLIP-style softmax loss.
export.encoder_type:
```
combined
```
exports one ONNX graph.
```
separate
```
exports independent vision and text graphs.
gen_trt_engine.tensorrt.data_type: TensorRT deployment supports
```
fp16
```
and
```
fp32
```
.

Hardware

Single-GPU training works for small datasets. Use 4+ GPUs for datasets with more than 100k images or large backbones. Use 16GB+ VRAM per GPU for small/fixed-resolution runs and larger GPUs for Radio-CLIP or high-resolution OpenCLIP variants.

Error Patterns

CUDA out of memory: Reduce

dataset.train.batch_size

dataset.val.batch_size

, or the TensorRT opt/max batch sizes. For export/deploy, check

export.input_height

and

export.input_width

against the selected fixed-resolution backbone.

NaN loss: Learning rate is too high for fine-tuning. Reduce

train.optim.vision_lr

and

train.optim.text_lr

, increase

train.optim.warmup_steps

, and verify that captions are valid non-empty text.

Zero retrieval or classification quality: Check that captions and prompts match the target label vocabulary. CLIP compares image and text embeddings, so prompt wording matters.

Dataset size smaller than total batch size: The total batch size is

batch_size * num_gpus

. If the dataset, especially validation, has fewer samples than this, reduce

dataset.val.batch_size

dataset.train.batch_size

Radio-CLIP config validation error: Set

model.adaptor_name

explicitly to

siglip

clip

Naflex export failure:

siglip2-so400m-patch16-naflex

is training-only in the current TAO docs and cannot be exported to ONNX or TensorRT. Use a fixed-resolution variant such as

siglip2-so400m-patch16-384

ONNX external data missing: Models larger than 2 GB export an ONNX file plus an external data file. Keep both files in the same directory and do not rename the external data file before

gen_trt_engine

TensorRT shape mismatch: When using dynamic batch export, provide min/opt/max shape profiles for every input. Text sequence length must match the tokenizer length, commonly 77 for CLIP tokenizers and 64 for SigLIP2 tokenizers.

attention_mask warning:

attention_mask

is currently accepted by exported graphs for compatibility, but TAO ignores its values and may remove it in a future release. Do not build new direct-ONNX inference code that depends on mask values.

Error merging spec.yaml with schema: A Hydra/OmegaConf config validation error. Common causes are putting

num_epochs

num_gpus

at the spec root instead of under

train.*

, or mixing up training image size (

model.image_size

) with export dimensions (

export.input_height

and

export.input_width

Spec Param / Parent Model Inference

Model-specific inference mappings belong in this MD file, not in

config.json

. Generated runners should read this section and apply the mappings with SDK helpers before

create_job()

. This mirrors the old microservices

infer_params.py

flow.

Inference mappings from TAO Core

clip.config.json

Action	Spec Field	Inference Function	Meaning
evaluate	`encryption_key`	`key`	encryption key
evaluate	`evaluate.checkpoint`	`parent_model`	model file inferred from the parent job results folder
evaluate	`evaluate.trt_engine`	`parent_model`	model file inferred from the parent job results folder
evaluate	`results_dir`	`output_dir`	current job results directory
export	`encryption_key`	`key`	encryption key
export	`export.checkpoint`	`parent_model`	model file inferred from the parent job results folder
export	`export.onnx_file`	`create_onnx_file`	output ONNX path
export	`results_dir`	`output_dir`	current job results directory
gen_trt_engine	`encryption_key`	`key`	encryption key
gen_trt_engine	`gen_trt_engine.onnx_file`	`parent_model`	model file inferred from the parent job results folder
gen_trt_engine	`gen_trt_engine.trt_engine`	`create_engine_file`	output TensorRT engine path
gen_trt_engine	`results_dir`	`output_dir`	current job results directory
inference	`encryption_key`	`key`	encryption key
inference	`inference.checkpoint`	`parent_model`	model file inferred from the parent job results folder
inference	`inference.trt_engine`	`parent_model`	model file inferred from the parent job results folder
inference	`results_dir`	`output_dir`	current job results directory
train	`encryption_key`	`key`	encryption key
train	`results_dir`	`output_dir`	current job results directory
train	`train.pretrained_model_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`train.resume_training_checkpoint_path`	`resume_model`	model file inferred from the current job results folder

For

parent_model

parent_model_folder

, pass the upstream train/export/AutoML child job id as

parent_job_id

. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to

config.json

and do not patch generated runner scripts to guess checkpoint paths.

tao-finetune-clip

NPX Install

Tags

SKILL.md Content

CLIP

Train Action Policy

Instructions

Training Requirements

Supported Models

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Deploy Workflow

Important Parameters

Hardware

Error Patterns

Spec Param / Parent Model Inference