← MIC coverage of Transformers model types

docling-project/docling-layout-heron

huggingface-mic/docling-layout-heron-mic@a4c39589d12b ← docling-project/docling-layout-heron@8f39ad3c0b4c · RTDetrV2ForObjectDetection · random weights

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Original42.8Mparameters

Fixture10.9Mparameters

Layer stacks11compared

Findings0to review

✓ Same architecture, smaller

Every module class, layer kind and checked property of the original is in the fixture.

Layer stacks

One square per layer, colored by kind: the layer's module classes and type attributes. Hover a square for its index and kind.

model.backbone.model.encoder.stages

Original · 4

Fixture · 4

Kind	Original	Fixture
no AvgPool2d	1	1	✓ kept
AvgPool2d	3	3	✓ kept

model.encoder_input_proj

Original · 3

Fixture · 3

Kind	Original	Fixture
Conv2d	3	3	✓ kept

model.encoder.aifi

Original · 1

Fixture · 1

Kind	Original	Fixture
AIFILayer	1	1	✓ kept

model.encoder.lateral_convs

Original · 2

Fixture · 2

Kind	Original	Fixture
ConvNormLayer	2	2	✓ kept

model.encoder.fpn_blocks

Original · 2

Fixture · 2

Kind	Original	Fixture
CSPRepLayer	2	2	✓ kept

model.encoder.downsample_convs

Original · 2

Fixture · 2

Kind	Original	Fixture
ConvNormLayer	2	2	✓ kept

model.encoder.pan_blocks

Original · 2

Fixture · 2

Kind	Original	Fixture
CSPRepLayer	2	2	✓ kept

model.decoder_input_proj

Original · 3

Fixture · 3

Kind	Original	Fixture
Conv2d	3	3	✓ kept

model.decoder.layers

Original · 6

Fixture · 2

Kind	Original	Fixture
DecoderLayer	6	2	✓ kept

model.decoder.bbox_embed

Original · 6

Fixture · 2

Kind	Original	Fixture
Linear	6	2	✓ kept

bbox_embed

Original · 6

Fixture · 2

Kind	Original	Fixture
Linear	6	2	✓ kept

Where the parameters are

Share of each model's parameters per component. Vocabulary and patch sizes are kept, so embeddings usually take a larger share of the fixture.

Original

Fixture

Component	Original	Share	Fixture	Share
model.backbone.model.encoder.stages	23.4M	54.8%	32.4K	0.3%
model.decoder.layers	6.0M	14.0%	102.6K	0.9%
model.encoder.pan_blocks	4.5M	10.4%	4.5M	41.0%
model.encoder.fpn_blocks	4.5M	10.4%	4.5M	41.0%
model.encoder.downsample_convs	1.2M	2.8%	1.2M	10.8%
model.encoder_input_proj	919.0K	2.1%	58.9K	0.5%
model.decoder.bbox_embed	795.7K	1.9%	17.2K	0.2%
9 others	1.5M	3.5%	566.5K	5.2%

Preserved properties

Config	Property	Original	Fixture
model	model_type	rt_detr_v2	rt_detr_v2	✓ kept
model	tie_word_embeddings	True	True	✓ kept
backbone_config	model_type	rt_detr_resnet	rt_detr_resnet	✓ kept
backbone_config	hidden_act	relu	relu	✓ kept
backbone_config	num_channels	3	3	✓ kept

Module classes

Instances of each class in the built model (prefix RTDetrV2 omitted).

Class	Original	Fixture
Linear	121	49
Conv2d	97	61
ReLU	57	17
Identity	56	32
FrozenBatchNorm2d	55	19
RTDetrResNetConvLayer	51	15
BatchNorm2d	42	42
ConvNormLayer	36	36
Sequential	35	23
ModuleList	28	20
SiLUActivation	24	24
LayerNorm	21	9
RTDetrResNetBottleNeckLayer	16	4
MLPPredictionHead	14	6
RepVggBlock	12	12
MLP	7	3
SelfAttention	7	3
DecoderLayer	6	2
MultiscaleDeformableAttention	6	2
RTDetrResNetShortCut	4	4
RTDetrResNetStage	4	4
CSPRepLayer	4	4
AvgPool2d	3	3
Embedding	1	1
GELUActivation	1	1
MaxPool2d	1	1
RTDetrResNetBackbone	1	1
RTDetrResNetEmbeddings	1	1
RTDetrResNetEncoder	1	1
AIFILayer	1	1
ConvEncoder	1	1
Decoder	1	1
EncoderLayer	1	1
ForObjectDetection	1	1
HybridEncoder	1	1
Model	1	1
SinePositionEmbedding	1	1

Config changes

9 fields changed, 74 unchanged.

Field	Original	Fixture
backbone_config.depths	[3, 4, 6, 3]	[1, 1, 1, 1]
backbone_config.hidden_sizes	[256, 512, 1024, 2048]	[16, 32, 64, 128]
d_model	256	64
decoder_attention_heads	8	4
decoder_ffn_dim	1024	128
decoder_layers	6	2
encoder_attention_heads	8	4
encoder_ffn_dim	1024	128
encoder_in_channels	[512, 1024, 2048]	[32, 64, 128]