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tiny-random/
sam3

Name: sam3
Rating: 2.5 (246 reviews)

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This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from facebook/sam3.

Example usage:

python

import requests
import torch
from PIL import Image
from transformers import Sam3Model, Sam3Processor
from transformers.models.sam3.modeling_sam3 import Sam3Config

model_id = "tiny-random/sam3"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = Sam3Model.from_pretrained(model_id).to(device)
processor = Sam3Processor.from_pretrained(model_id)

kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg"
kitchen_image = Image.open(requests.get(
    kitchen_url, stream=True).raw).convert("RGB")
# Segment "handle" but exclude the oven handle using a negative box
text = "handle"
# Negative box covering oven handle area (xyxy): [40, 183, 318, 204]
oven_handle_box = [40, 183, 318, 204]
input_boxes = [[oven_handle_box]]
inputs = processor(
    images=kitchen_image,
    text=text,
    input_boxes=input_boxes,
    input_boxes_labels=[[0]],  # 0 = negative (exclude this region)
    return_tensors="pt"
).to(device)
with torch.no_grad():
    outputs = model(**inputs)
# Post-process results
results = processor.post_process_instance_segmentation(
    outputs,
    threshold=0.5,
    mask_threshold=0.5,
    target_sizes=inputs.get("original_sizes").tolist()
)[0]
print(results)
# This will segment pot handles but exclude the oven handle

Codes to create this repo:

python

import json
from pathlib import Path

import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoProcessor,
    GenerationConfig,
    Sam3Processor,
    set_seed,
)
from transformers.models.sam3.modeling_sam3 import Sam3Config, Sam3Model

source_model_id = "facebook/sam3"
save_folder = "/tmp/tiny-random/sam3"

processor = Sam3Processor.from_pretrained(
    source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)

with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
    config_json = json.load(f)
HIDDEN_SIZE = 16
INTERMEDIATE_SIZE = 32
NUM_ATTENTION_HEADS = 2
config_json['detector_config']['detr_decoder_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
})
config_json['detector_config']['detr_encoder_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
})
config_json['detector_config']['geometry_encoder_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
})
config_json['detector_config']['mask_decoder_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
})
config_json['detector_config']['text_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
    'projection_dim': HIDDEN_SIZE,
    'num_hidden_layers': 2,
})
config_json['detector_config']['vision_config']['backbone_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
    'fpn_hidden_size': HIDDEN_SIZE,
    'global_attn_indexes': [1, 3, 5, 7],
    'num_hidden_layers': 8,
})
config_json['detector_config']['vision_config'].update({
    'fpn_hidden_size': HIDDEN_SIZE,
})
config_json['tracker_config']['mask_decoder_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'iou_head_hidden_dim': HIDDEN_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
})
config_json['tracker_config'].update({
    'mask_downsampler_embed_dim': HIDDEN_SIZE,
    'memory_attention_feed_forward_hidden_size': HIDDEN_SIZE,
    'memory_attention_hidden_size': HIDDEN_SIZE,
    'memory_encoder_hidden_size': HIDDEN_SIZE,
    'memory_fuser_embed_dim': HIDDEN_SIZE,
    'memory_fuser_intermediate_dim': INTERMEDIATE_SIZE,
})
config_json['tracker_config']['prompt_encoder_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
})
config_json['tracker_config']['vision_config']['backbone_config'].update({
    'hidden_size': HIDDEN_SIZE,
    'intermediate_size': INTERMEDIATE_SIZE,
    'num_attention_heads': NUM_ATTENTION_HEADS,
    'global_attn_indexes': [1, 3, 5, 7],
    'num_hidden_layers': 8,
})
config_json['tracker_config']['vision_config'].update({
    'fpn_hidden_size': HIDDEN_SIZE,
})

with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
    json.dump(config_json, f, indent=2)

config = Sam3Config.from_pretrained(
    save_folder,
    trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.float32)
model = Sam3Model(config)
set_seed(42)
model = model.cpu()
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        torch.nn.init.normal_(p, 0, 0.1)
        print(name, p.shape)
model.save_pretrained(save_folder)
# print(list(model.state_dict().keys()))
# there is some bug in model.save_pretrained... Re-save the model weights here.
import safetensors.torch
safetensors.torch.save_file(
    tensors=model.state_dict(),
    filename=f"{save_folder}/model.safetensors"
)

Printing the model:

text

Sam3Model(
  (vision_encoder): Sam3VisionModel(
    (backbone): Sam3ViTModel(
      (embeddings): Sam3ViTEmbeddings(
        (patch_embeddings): Sam3ViTPatchEmbeddings(
          (projection): Conv2d(3, 16, kernel_size=(14, 14), stride=(14, 14), bias=False)
        )
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (layer_norm): LayerNorm((16,), eps=1e-06, elementwise_affine=True)
      (layers): ModuleList(
        (0-7): 8 x Sam3ViTLayer(
          (layer_norm1): LayerNorm((16,), eps=1e-06, elementwise_affine=True)
          (rotary_emb): Sam3ViTRotaryEmbedding()
          (attention): Sam3ViTRoPEAttention(
            (q_proj): Linear(in_features=16, out_features=16, bias=True)
            (k_proj): Linear(in_features=16, out_features=16, bias=True)
            (v_proj): Linear(in_features=16, out_features=16, bias=True)
            (o_proj): Linear(in_features=16, out_features=16, bias=True)
          )
          (layer_norm2): LayerNorm((16,), eps=1e-06, elementwise_affine=True)
          (mlp): Sam3MLP(
            (activation_fn): GELUActivation()
            (fc1): Linear(in_features=16, out_features=32, bias=True)
            (fc2): Linear(in_features=32, out_features=16, bias=True)
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
    )
    (neck): Sam3VisionNeck(
      (position_encoding): Sam3SinePositionEmbedding()
      (fpn_layers): ModuleList(
        (0): Sam3FPNLayer(
          (scale_layers): ModuleList(
            (0): ConvTranspose2d(16, 8, kernel_size=(2, 2), stride=(2, 2))
            (1): GELU(approximate='none')
            (2): ConvTranspose2d(8, 4, kernel_size=(2, 2), stride=(2, 2))
          )
          (proj1): Conv2d(4, 16, kernel_size=(1, 1), stride=(1, 1))
          (proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (1): Sam3FPNLayer(
          (scale_layers): ModuleList(
            (0): ConvTranspose2d(16, 8, kernel_size=(2, 2), stride=(2, 2))
          )
          (proj1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1))
          (proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (2): Sam3FPNLayer(
          (scale_layers): ModuleList()
          (proj1): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1))
          (proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (3): Sam3FPNLayer(
          (scale_layers): ModuleList(
            (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
          )
          (proj1): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1))
          (proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
    )
  )
  (text_encoder): CLIPTextModelWithProjection(
    (text_model): CLIPTextTransformer(
      (embeddings): CLIPTextEmbeddings(
        (token_embedding): Embedding(49408, 16)
        (position_embedding): Embedding(32, 16)
      )
      (encoder): CLIPEncoder(
        (layers): ModuleList(
          (0-1): 2 x CLIPEncoderLayer(
            (self_attn): CLIPAttention(
              (k_proj): Linear(in_features=16, out_features=16, bias=True)
              (v_proj): Linear(in_features=16, out_features=16, bias=True)
              (q_proj): Linear(in_features=16, out_features=16, bias=True)
              (out_proj): Linear(in_features=16, out_features=16, bias=True)
            )
            (layer_norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
            (mlp): CLIPMLP(
              (activation_fn): GELUActivation()
              (fc1): Linear(in_features=16, out_features=32, bias=True)
              (fc2): Linear(in_features=32, out_features=16, bias=True)
            )
            (layer_norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
          )
        )
      )
      (final_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    )
    (text_projection): Linear(in_features=16, out_features=16, bias=False)
  )
  (text_projection): Linear(in_features=16, out_features=16, bias=True)
  (geometry_encoder): Sam3GeometryEncoder(
    (position_encoding): Sam3SinePositionEmbedding()
    (label_embed): Embedding(2, 16)
    (cls_embed): Embedding(1, 16)
    (boxes_direct_project): Linear(in_features=4, out_features=16, bias=True)
    (boxes_pool_project): Conv2d(16, 16, kernel_size=(7, 7), stride=(1, 1))
    (boxes_pos_enc_project): Linear(in_features=18, out_features=16, bias=True)
    (vision_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    (final_proj): Linear(in_features=16, out_features=16, bias=True)
    (prompt_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    (layers): ModuleList(
      (0-2): 3 x Sam3GeometryEncoderLayer(
        (layer_norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (self_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (dropout): Dropout(p=0.1, inplace=False)
        (cross_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (layer_norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (mlp): Sam3MLP(
          (activation_fn): ReLU()
          (fc1): Linear(in_features=16, out_features=32, bias=True)
          (fc2): Linear(in_features=32, out_features=16, bias=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
        (layer_norm3): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
      )
    )
    (output_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
  )
  (detr_encoder): Sam3DetrEncoder(
    (layers): ModuleList(
      (0-5): 6 x Sam3DetrEncoderLayer(
        (layer_norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (self_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (dropout): Dropout(p=0.1, inplace=False)
        (cross_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (layer_norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (mlp): Sam3MLP(
          (activation_fn): ReLU()
          (fc1): Linear(in_features=16, out_features=32, bias=True)
          (fc2): Linear(in_features=32, out_features=16, bias=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
        (layer_norm3): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
      )
    )
  )
  (detr_decoder): Sam3DetrDecoder(
    (layers): ModuleList(
      (0-5): 6 x Sam3DetrDecoderLayer(
        (self_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (self_attn_dropout): Dropout(p=0.1, inplace=False)
        (self_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (text_cross_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (text_cross_attn_dropout): Dropout(p=0.1, inplace=False)
        (text_cross_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (vision_cross_attn): Sam3Attention(
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (o_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (vision_cross_attn_dropout): Dropout(p=0.1, inplace=False)
        (vision_cross_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (mlp): Sam3MLP(
          (activation_fn): ReLU()
          (fc1): Linear(in_features=16, out_features=32, bias=True)
          (fc2): Linear(in_features=32, out_features=16, bias=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
        (mlp_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (mlp_dropout): Dropout(p=0.1, inplace=False)
      )
    )
    (output_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    (box_head): Sam3DecoderMLP(
      (layer1): Linear(in_features=16, out_features=16, bias=True)
      (layer2): Linear(in_features=16, out_features=16, bias=True)
      (layer3): Linear(in_features=16, out_features=4, bias=True)
    )
    (query_embed): Embedding(200, 16)
    (reference_points): Embedding(200, 4)
    (presence_token): Embedding(1, 16)
    (presence_head): Sam3DecoderMLP(
      (layer1): Linear(in_features=16, out_features=16, bias=True)
      (layer2): Linear(in_features=16, out_features=16, bias=True)
      (layer3): Linear(in_features=16, out_features=1, bias=True)
    )
    (presence_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    (ref_point_head): Sam3DecoderMLP(
      (layer1): Linear(in_features=32, out_features=16, bias=True)
      (layer2): Linear(in_features=16, out_features=16, bias=True)
    )
    (box_rpb_embed_x): Sam3DecoderMLP(
      (layer1): Linear(in_features=2, out_features=16, bias=True)
      (layer2): Linear(in_features=16, out_features=2, bias=True)
    )
    (box_rpb_embed_y): Sam3DecoderMLP(
      (layer1): Linear(in_features=2, out_features=16, bias=True)
      (layer2): Linear(in_features=16, out_features=2, bias=True)
    )
    (position_encoding): Sam3SinePositionEmbedding()
  )
  (mask_decoder): Sam3MaskDecoder(
    (pixel_decoder): Sam3PixelDecoder(
      (conv_layers): ModuleList(
        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (norms): ModuleList(
        (0-2): 3 x GroupNorm(8, 16, eps=1e-05, affine=True)
      )
    )
    (mask_embedder): Sam3MaskEmbedder(
      (layers): ModuleList(
        (0-2): 3 x Linear(in_features=16, out_features=16, bias=True)
      )
      (activation): ReLU()
    )
    (instance_projection): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1))
    (semantic_projection): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))
    (prompt_cross_attn): Sam3Attention(
      (q_proj): Linear(in_features=16, out_features=16, bias=True)
      (k_proj): Linear(in_features=16, out_features=16, bias=True)
      (v_proj): Linear(in_features=16, out_features=16, bias=True)
      (o_proj): Linear(in_features=16, out_features=16, bias=True)
    )
    (prompt_cross_attn_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    (prompt_cross_attn_dropout): Dropout(p=0.0, inplace=False)
  )
  (dot_product_scoring): Sam3DotProductScoring(
    (text_mlp): Sam3DecoderMLP(
      (layer1): Linear(in_features=16, out_features=32, bias=True)
      (layer2): Linear(in_features=32, out_features=16, bias=True)
    )
    (text_mlp_dropout): Dropout(p=0.1, inplace=False)
    (text_mlp_out_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
    (text_proj): Linear(in_features=16, out_features=16, bias=True)
    (query_proj): Linear(in_features=16, out_features=16, bias=True)
  )
)