Sam 3 Segmentation Fine Tuning

Does sam3 not support segmentation fine tuning at this moment?

When I use the following config:
# @package _global_
defaults:
- _self_

# ============================================================================
# Paths Configuration (Chage this to your own paths)
# ============================================================================
paths:
roboflow_vl_100_root: /home/ubuntu/sam/custom-root
experiment_log_dir: /home/ubuntu/sam/custom-root/logs
bpe_path: /home/ubuntu/sam/sam3/assets/bpe_simple_vocab_16e6.txt.gz

# Roboflow dataset configuration
roboflow_train:
num_images: 100 # Note: This is the number of images used for training. If null, all images are used.
supercategory: ${all_roboflow_supercategories.${string:${submitit.job_array.task_index}}}

# Training transforms pipeline
train_transforms:
- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
transforms:
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
query_filter:
_target_: sam3.train.transforms.filter_query_transforms.FilterCrowds
- _target_: sam3.train.transforms.point_sampling.RandomizeInputBbox
box_noise_std: 0.1
box_noise_max: 20
- _target_: sam3.train.transforms.segmentation.DecodeRle
- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
sizes:
_target_: sam3.train.transforms.basic.get_random_resize_scales
size: ${scratch.resolution}
min_size: 480
rounded: false
max_size:
_target_: sam3.train.transforms.basic.get_random_resize_max_size
size: ${scratch.resolution}
square: true
consistent_transform: ${scratch.consistent_transform}
- _target_: sam3.train.transforms.basic_for_api.PadToSizeAPI
size: ${scratch.resolution}
consistent_transform: ${scratch.consistent_transform}
- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
query_filter:
_target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets
- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
mean: ${scratch.train_norm_mean}
std: ${scratch.train_norm_std}
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
query_filter:
_target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
query_filter:
_target_: sam3.train.transforms.filter_query_transforms.FilterFindQueriesWithTooManyOut
max_num_objects: ${scratch.max_ann_per_img}

# Validation transforms pipeline
val_transforms:
- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
transforms:
- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
sizes: ${scratch.resolution}
max_size:
_target_: sam3.train.transforms.basic.get_random_resize_max_size
size: ${scratch.resolution}
square: true
consistent_transform: False
- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
mean: ${scratch.train_norm_mean}
std: ${scratch.train_norm_std}

# loss config (no mask loss)
# loss:
# _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper
# matcher: ${scratch.matcher}
# o2m_weight: 2.0
# o2m_matcher:
# _target_: sam3.train.matcher.BinaryOneToManyMatcher
# alpha: 0.3
# threshold: 0.4
# topk: 4
# use_o2m_matcher_on_o2m_aux: false # Another option is true
# loss_fns_find:
# - _target_: sam3.train.loss.loss_fns.Boxes
# weight_dict:
# loss_bbox: 5.0
# loss_giou: 2.0
# - _target_: sam3.train.loss.loss_fns.IABCEMdetr
# weak_loss: False
# weight_dict:
# loss_ce: 20.0 # Another option is 100.0
# presence_loss: 20.0
# pos_weight: 10.0 # Another option is 5.0
# alpha: 0.25
# gamma: 2
# use_presence: True # Change
# pos_focal: false
# pad_n_queries: 200
# pad_scale_pos: 1.0

# loss_fn_semantic_seg: null
# scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}

# NOTE: Loss to be used for training in case of segmentation
loss:
_target_: sam3.train.loss.sam3_loss.Sam3LossWrapper
matcher: ${scratch.matcher}
o2m_weight: 2.0
o2m_matcher:
_target_: sam3.train.matcher.BinaryOneToManyMatcher
alpha: 0.3
threshold: 0.4
topk: 4
use_o2m_matcher_on_o2m_aux: false
loss_fns_find:
- _target_: sam3.train.loss.loss_fns.Boxes
weight_dict:
loss_bbox: 5.0
loss_giou: 2.0
- _target_: sam3.train.loss.loss_fns.IABCEMdetr
weak_loss: False
weight_dict:
loss_ce: 20.0 # Another option is 100.0
presence_loss: 20.0
pos_weight: 10.0 # Another option is 5.0
alpha: 0.25
gamma: 2
use_presence: True # Change
pos_focal: false
pad_n_queries: 200
pad_scale_pos: 1.0
- _target_: sam3.train.loss.loss_fns.Masks
focal_alpha: 0.25
focal_gamma: 2.0
weight_dict:
loss_mask: 200.0
loss_dice: 10.0
compute_aux: false
loss_fn_semantic_seg:
_target_: sam3.train.loss.loss_fns.SemanticSegCriterion
presence_head: True
presence_loss: False # Change
focal: True
focal_alpha: 0.6
focal_gamma: 2.0
downsample: False
weight_dict:
loss_semantic_seg: 20.0
loss_semantic_presence: 1.0
loss_semantic_dice: 30.0
scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}

# ============================================================================
# Different helper parameters and functions
# ============================================================================
scratch:
enable_segmentation: True # NOTE: This is the number of queries used for segmentation
# Model parameters
d_model: 256
pos_embed:
_target_: sam3.model.position_encoding.PositionEmbeddingSine
num_pos_feats: ${scratch.d_model}
normalize: true
scale: null
temperature: 10000

# Box processing
use_presence_eval: True
original_box_postprocessor:
_target_: sam3.eval.postprocessors.PostProcessImage
max_dets_per_img: -1 # infinite detections
use_original_ids: true
use_original_sizes_box: true
use_presence: ${scratch.use_presence_eval}

# Matcher configuration
matcher:
_target_: sam3.train.matcher.BinaryHungarianMatcherV2
focal: true # with `focal: true` it is equivalent to BinaryFocalHungarianMatcher
cost_class: 2.0
cost_bbox: 5.0
cost_giou: 2.0
alpha: 0.25
gamma: 2
stable: False
scale_by_find_batch_size: True

# Image processing parameters
resolution: 1008
consistent_transform: False
max_ann_per_img: 200

# Normalization parameters
train_norm_mean: [0.5, 0.5, 0.5]
train_norm_std: [0.5, 0.5, 0.5]
val_norm_mean: [0.5, 0.5, 0.5]
val_norm_std: [0.5, 0.5, 0.5]

# Training parameters
num_train_workers: 2
num_val_workers: 1
max_data_epochs: 20
target_epoch_size: 1500
hybrid_repeats: 1
context_length: 2
gather_pred_via_filesys: false

# Learning rate and scheduler parameters
lr_scale: 0.1
lr_transformer: ${times:8e-4,${scratch.lr_scale}}
lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}
lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}
lrd_vision_backbone: 0.9
wd: 0.1
scheduler_timescale: 20
scheduler_warmup: 20
scheduler_cooldown: 20

val_batch_size: 1
collate_fn_val:
_target_: sam3.train.data.collator.collate_fn_api
_partial_: true
repeats: ${scratch.hybrid_repeats}
dict_key: roboflow100
with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!

gradient_accumulation_steps: 1
train_batch_size: 1
collate_fn:
_target_: sam3.train.data.collator.collate_fn_api
_partial_: true
repeats: ${scratch.hybrid_repeats}
dict_key: all
with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!

# ============================================================================
# Trainer Configuration
# ============================================================================

trainer:
_target_: sam3.train.trainer.Trainer
skip_saving_ckpts: False
empty_gpu_mem_cache_after_eval: True
skip_first_val: False
max_epochs: 20
accelerator: cuda
seed_value: 123
val_epoch_freq: 1
mode: train
gradient_accumulation_steps: ${scratch.gradient_accumulation_steps}

distributed:
backend: nccl
find_unused_parameters: True
gradient_as_bucket_view: True

loss:
all: ${roboflow_train.loss}
default:
_target_: sam3.train.loss.sam3_loss.DummyLoss

data:
train:
_target_: sam3.train.data.torch_dataset.TorchDataset
dataset:
_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
limit_ids: ${roboflow_train.num_images}
transforms: ${roboflow_train.train_transforms}
load_segmentation: ${scratch.enable_segmentation}
max_ann_per_img: 500000
multiplier: 1
max_train_queries: 50000
max_val_queries: 50000
training: true
use_caching: False
img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/
ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/_annotations.coco.json

shuffle: True
batch_size: ${scratch.train_batch_size}
num_workers: ${scratch.num_train_workers}
pin_memory: True
drop_last: True
collate_fn: ${scratch.collate_fn}

val:
_target_: sam3.train.data.torch_dataset.TorchDataset
dataset:
_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
load_segmentation: ${scratch.enable_segmentation}
coco_json_loader:
_target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON
include_negatives: true
category_chunk_size: 2 # Note: You can increase this based on the memory of your GPU.
_partial_: true
img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/val/
ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/val/_annotations.coco.json
transforms: ${roboflow_train.val_transforms}
max_ann_per_img: 100000
multiplier: 1
training: false

shuffle: False
batch_size: ${scratch.val_batch_size}
num_workers: ${scratch.num_val_workers}
pin_memory: True
drop_last: False
collate_fn: ${scratch.collate_fn_val}

model:
_target_: sam3.model_builder.build_sam3_image_model
bpe_path: ${paths.bpe_path}
device: cpus
eval_mode: False
enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.

meters:
val:
roboflow100:
segmentation:
_target_: sam3.eval.coco_writer.PredictionDumper
iou_type: "segm"
dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${roboflow_train.supercategory}
merge_predictions: True
postprocessor: ${scratch.original_box_postprocessor}
gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}
maxdets: 800
pred_file_evaluators:
- _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators
gt_path: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/val/_annotations.coco.json
tide: False
iou_type: "segm"

optim:
amp:
enabled: True
amp_dtype: bfloat16

optimizer:
_target_: torch.optim.AdamW

gradient_clip:
_target_: sam3.train.optim.optimizer.GradientClipper
max_norm: 0.1
norm_type: 2

param_group_modifiers:
- _target_: sam3.train.optim.optimizer.layer_decay_param_modifier
_partial_: True
layer_decay_value: ${scratch.lrd_vision_backbone}
apply_to: 'backbone.vision_backbone.trunk'
overrides:
- pattern: '*pos_embed*'
value: 1.0

options:
lr:
- scheduler: # transformer and class_embed
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
base_lr: ${scratch.lr_transformer}
timescale: ${scratch.scheduler_timescale}
warmup_steps: ${scratch.scheduler_warmup}
cooldown_steps: ${scratch.scheduler_cooldown}
- scheduler:
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
base_lr: ${scratch.lr_vision_backbone}
timescale: ${scratch.scheduler_timescale}
warmup_steps: ${scratch.scheduler_warmup}
cooldown_steps: ${scratch.scheduler_cooldown}
param_names:
- 'backbone.vision_backbone.*'
- scheduler:
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
base_lr: ${scratch.lr_language_backbone}
timescale: ${scratch.scheduler_timescale}
warmup_steps: ${scratch.scheduler_warmup}
cooldown_steps: ${scratch.scheduler_cooldown}
param_names:
- 'backbone.language_backbone.*'

weight_decay:
- scheduler:
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
value: ${scratch.wd}
- scheduler:
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
value: 0.0
param_names:
- '*bias*'
module_cls_names: ['torch.nn.LayerNorm']

checkpoint:
save_dir: ${launcher.experiment_log_dir}/checkpoints
save_freq: 5 # 0 only last checkpoint is saved.

logging:
tensorboard_writer:
_target_: sam3.train.utils.logger.make_tensorboard_logger
log_dir: ${launcher.experiment_log_dir}/tensorboard
flush_secs: 120
should_log: True
wandb_writer: null
log_dir: ${launcher.experiment_log_dir}/logs/${roboflow_train.supercategory}
log_freq: 10

# ============================================================================
# Launcher and Submitit Configuration
# ============================================================================

launcher:
num_nodes: 1
gpus_per_node: 1
experiment_log_dir: ${paths.experiment_log_dir}
multiprocessing_context: forkserver

submitit:
account: null
partition: null
qos: null
timeout_hour: 72
use_cluster: True
cpus_per_task: 10
port_range: [10000, 65000]
constraint: null
# Uncomment for job array configuration
job_array:
num_tasks: 1
task_index: 0

# ============================================================================
# Available Roboflow Supercategories (for reference)
# ============================================================================

all_roboflow_supercategories:
- z22

The code fail with predictions has no masks key during validation.
But during training time I am observing that we do have train segmentation loss also present.
`{"Losses/train_all_loss": 291.40562232284805, "Losses/train_default_loss": 0, "Losses/train_all_loss_bbox": 0.04907124418765307, "Losses/train_all_loss_giou": 0.3097055367380381, "Losses/train_all_loss_bbox_o2m": 0.25392176304012537, "Losses/train_all_loss_giou_o2m": 1.178743647634983, "Losses/train_all_loss_ce": 0.017840963501075748, "Losses/train_all_ce_f1": 0.23937933709472417, "Losses/train_all_presence_loss": 0.1593843568693046, "Losses/train_all_presence_dec_acc": 0.9228571724891662, "Losses/train_all_loss_ce_o2m": 0.09227114700712263, "Losses/train_all_ce_f1_o2m": 0.4906483779847622, "Losses/train_all_presence_loss_o2m": 0.0, "Losses/train_all_presence_dec_acc_o2m": 0.0, "Losses/train_all_loss_mask": 0.003172511872544419, "Losses/train_all_loss_dice": 0.29496058978140355, "Losses/train_all_loss_mask_o2m": 0.02098744154907763, "Losses/train_all_loss_dice_o2m": 1.1210279282927513, "Losses/train_all_loss_bbox_aux_0": 0.06682779693976044, "Losses/train_all_loss_giou_aux_0": 0.4081690326333046, "Losses/train_all_loss_bbox_aux_0_o2m": 0.14454417156055568, "Losses/train_all_loss_giou_aux_0_o2m": 0.8131714349985123, "Losses/train_all_loss_ce_aux_0": 0.016816309143323452, "Losses/train_all_ce_f1_aux_0": 0.2018275634199381, "Losses/train_all_presence_loss_aux_0": 0.15791587307904592, "Losses/train_all_presence_dec_acc_aux_0": 0.9207143145799637, "Losses/train_all_loss_ce_aux_0_o2m": 0.03715709789190441, "Losses/train_all_ce_f1_aux_0_o2m": 0.22506245568394662, "Losses/train_all_presence_loss_aux_0_o2m": 0.0, "Losses/train_all_presence_dec_acc_aux_0_o2m": 0.0, "Losses/train_all_loss_bbox_aux_1": 0.05492462095804512, "Losses/train_all_loss_giou_aux_1": 0.33395033814013003, "Losses/train_all_loss_bbox_aux_1_o2m": 0.12702802307903766, "Losses/train_all_loss_giou_aux_1_o2m": 0.7043332879245281, "Losses/train_all_loss_ce_aux_1": 0.01780572710733395, "Losses/train_all_ce_f1_aux_1": 0.20777562160044907, "Losses/train_all_presence_loss_aux_1": 0.15562092261569888, "Losses/train_all_presence_dec_acc_aux_1": 0.9200000303983689, "Losses/train_all_loss_ce_aux_1_o2m": 0.03819340365938842, "Losses/train_all_ce_f1_aux_1_o2m": 0.2233561248332262, "Losses/train_all_presence_loss_aux_1_o2m": 0.0, "Losses/train_all_presence_dec_acc_aux_1_o2m": 0.0, "Losses/train_all_loss_bbox_aux_2": 0.05241847197525203, "Losses/train_all_loss_giou_aux_2": 0.3214942040294409, "Losses/train_all_loss_bbox_aux_2_o2m": 0.12400685776025057, "Losses/train_all_loss_giou_aux_2_o2m": 0.6849668900668621, "Losses/train_all_loss_ce_aux_2": 0.01778830372611992, "Losses/train_all_ce_f1_aux_2": 0.23926191195845603, "Losses/train_all_presence_loss_aux_2": 0.15706820492800033, "Losses/train_all_presence_dec_acc_aux_2": 0.9235714572668076, "Losses/train_all_loss_ce_aux_2_o2m": 0.03885760723147541, "Losses/train_all_ce_f1_aux_2_o2m": 0.23316837422549724, "Losses/train_all_presence_loss_aux_2_o2m": 0.0, "Losses/train_all_presence_dec_acc_aux_2_o2m": 0.0, "Losses/train_all_loss_bbox_aux_3": 0.05105132736265659, "Losses/train_all_loss_giou_aux_3": 0.31483356446027755, "Losses/train_all_loss_bbox_aux_3_o2m": 0.11894042745232582, "Losses/train_all_loss_giou_aux_3_o2m": 0.6671358375251293, "Losses/train_all_loss_ce_aux_3": 0.01781949920405168, "Losses/train_all_ce_f1_aux_3": 0.2319374280422926, "Losses/train_all_presence_loss_aux_3": 0.1578532818506028, "Losses/train_all_presence_dec_acc_aux_3": 0.9228571724891662, "Losses/train_all_loss_ce_aux_3_o2m": 0.039612381584011015, "Losses/train_all_ce_f1_aux_3_o2m": 0.22166858714073898, "Losses/train_all_presence_loss_aux_3_o2m": 0.0, "Losses/train_all_presence_dec_acc_aux_3_o2m": 0.0, "Losses/train_all_loss_bbox_aux_4": 0.049818813037127256, "Losses/train_all_loss_giou_aux_4": 0.311868184953928, "Losses/train_all_loss_bbox_aux_4_o2m": 0.11900744654238224, "Losses/train_all_loss_giou_aux_4_o2m": 0.6695742666721344, "Losses/train_all_loss_ce_aux_4": 0.01783945863484405, "Losses/train_all_ce_f1_aux_4": 0.2419732666015625, "Losses/train_all_presence_loss_aux_4": 0.15830876644788078, "Losses/train_all_presence_dec_acc_aux_4": 0.9214286017417908, "Losses/train_all_loss_ce_aux_4_o2m": 0.03997141381725669, "Losses/train_all_ce_f1_aux_4_o2m": 0.22639159649610519, "Losses/train_all_presence_loss_aux_4_o2m": 0.0, "Losses/train_all_presence_dec_acc_aux_4_o2m": 0.0, "Losses/train_all_core_loss": 291.40562232284805, "Losses/train_all_loss_semantic_presence": 0.0, "Losses/train_all_presence_acc": 0.0, "Losses/train_all_loss_semantic_seg": 0.03686944677960127, "Losses/train_all_loss_semantic_dice": 0.3965323495678604, "Losses/train_all_miou_semantic_seg": 0.5273188393190503, "Trainer/where": 0.0495, "Trainer/epoch": 0, "Trainer/steps_train": 100}`

Do I need to change something in the meter attribute in the config?

Start by tracing the configured entry points in sam3.model_builder.build_sam3_image_model, sam3.train.data.sam3_image_dataset.Sam3ImageDataset, and sam3.train.loss.sam3_loss.Sam3LossWrapper. Run the supplied segmentation configuration and inspect the mask and semantic-loss paths, including Masks and SemanticSegCriterion. Done means establishing whether segmentation fine-tuning is supported and identifying a reproducible configuration or implementation change.

Written by the indexing model from the issue text.