Tao Finetune Clip

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 or 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 or 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 or inference.text_file.

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:

{
    "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:

{
    "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:

{
    "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:

{
    "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:

{
    "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:

{
    "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 or _text to 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 or dataset.train.batch_size.

Radio-CLIP config validation error: Set model.adaptor_name explicitly to siglip or 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 or 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 or 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.