Re-Identification
Person re-identification. Learns discriminative embeddings to match the same person across different camera views. Metric learning based.
Set model.pretrained_model_path for pretrained weights.
Dataclass Schemas
Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML still requires schemas/train.schema.json and references/spec_template_train.yaml to exist and parse. Use the packaged train schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.
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.
Training Requirements
- Dataset type: re_identification
- Formats: default
- Monitoring metric: cmc_rank_1
Per-Action Dataset Requirements
| Action | Spec Key | Source | Files | List? |
|---|---|---|---|---|
| evaluate | evaluate.test_dataset | train_datasets | sample_test.tar.gz | No |
| evaluate | evaluate.query_dataset | train_datasets | sample_query.tar.gz | No |
| inference | inference.test_dataset | train_datasets | sample_test.tar.gz | No |
| inference | inference.query_dataset | train_datasets | sample_query.tar.gz | No |
| train | dataset.train_dataset_dir | train_datasets | sample_train.tar.gz | No |
| train | dataset.test_dataset_dir | train_datasets | sample_test.tar.gz | No |
| train | dataset.query_dataset_dir | train_datasets | sample_query.tar.gz | No |
Typical Spec Overrides
Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.
S3_TRAIN = "s3://bucket/data/train"
train (mandatory data sources):
{
"train.num_epochs": 30,
"train.checkpoint_interval": 10,
"train.validation_interval": 10,
"train.num_gpus": 1,
"num_classes": 100,
"num_workers": 4,
"batch_size": 16,
"dataset.train_dataset_dir": f"{S3_TRAIN}/sample_train.tar.gz",
"dataset.test_dataset_dir": f"{S3_TRAIN}/sample_test.tar.gz",
"dataset.query_dataset_dir": f"{S3_TRAIN}/sample_query.tar.gz",
}
evaluate (mandatory data sources):
{
"evaluate.test_dataset": f"{S3_TRAIN}/sample_test.tar.gz",
"evaluate.query_dataset": f"{S3_TRAIN}/sample_query.tar.gz",
}
inference (mandatory data sources):
{
"inference.test_dataset": f"{S3_TRAIN}/sample_test.tar.gz",
"inference.query_dataset": f"{S3_TRAIN}/sample_query.tar.gz",
}
Eval Dataset
Required. Evaluation requires test and query datasets for retrieval-based metrics (CMC, mAP).
Important Parameters
- dataset.num_classes: Number of identities. Default 751. Must match the number of unique identities in training data.
- model.backbone: Default resnet_50.
- optim.base_lr: Base learning rate. Default 3.5e-4.
- dataset.batch_size: Per-GPU batch size. Default 64. Re-ID benefits from large batches for better triplet/contrastive sampling.
- dataset.num_instances: Number of instances per identity in a batch. Controls sampling strategy for metric learning.
Multi-GPU / Multi-Node
Launch method: Lightning-managed (single python process, Lightning spawns workers).
| Spec Key | Description | Default |
|---|---|---|
train.num_gpus | Number of GPUs | 1 |
train.gpu_ids | GPU device indices | [0] |
- Multi-GPU strategy:
ddp_find_unused_parameters_true sync_batchnormis always enabled- Precision forced to FP16 (
16-mixed) - No explicit
num_nodesconfig — single-node oriented
Hardware
Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Re-ID models are relatively lightweight but benefit from large batch sizes for metric learning.
Error Patterns
num_classes mismatch: Ensure dataset.num_classes equals the number of unique identity folders in the training set.
Query/gallery mismatch: Query and test (gallery) datasets must share the same identity namespace.
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 re_identification.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.output_cmc_curve_plot | create_evaluate_cmc_plot_reid | ReID CMC plot path |
| evaluate | evaluate.output_sampled_matches_plot | create_evaluate_matches_plot_reid | ReID sampled matches plot path |
| 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 |
| inference | encryption_key | key | encryption key |
| inference | inference.checkpoint | parent_model | model file inferred from the parent job results folder |
| inference | inference.output_file | create_inference_result_file_reid | ReID inference JSON path |
| inference | results_dir | output_dir | current job results directory |
| train | encryption_key | key | encryption key |
| train | model.pretrained_model_path | ptm_if_no_resume_model | PTM when no resume checkpoint exists |
| train | results_dir | output_dir | current job results directory |
| 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.