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fundamentalvision / Deformable-DETR

Deformable DETR: Deformable Transformers for End-to-End Object Detection.
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Fine tune on custom dataset - parts to remove #126

Open aviadlazar opened 2 years ago

aviadlazar commented 2 years ago

I'm interested in fine tuning a model trained on COCO for custom data, but I'm unsure about what layers/parts of the model should I remove to make the detection head compatible with the new dataset. Here is a list of components of r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage-checkpoint.pth:

transformer.level_embed
transformer.encoder.layers.0.self_attn.sampling_offsets.weight
transformer.encoder.layers.0.self_attn.sampling_offsets.bias
transformer.encoder.layers.0.self_attn.attention_weights.weight
transformer.encoder.layers.0.self_attn.attention_weights.bias
transformer.encoder.layers.0.self_attn.value_proj.weight
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transformer.encoder.layers.0.self_attn.output_proj.weight
transformer.encoder.layers.0.self_attn.output_proj.bias
transformer.encoder.layers.0.norm1.weight
transformer.encoder.layers.0.norm1.bias
transformer.encoder.layers.0.linear1.weight
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It seems that its easier to tell that some parts should be removed, like transformer.decoder.class_embed.0.weight (I could still be wrong here), but there are other parts I'm unsure about, like class_embed.0.weight.

If anyone has solved this before and is willing to share what they have done it would be helpful for anyone who wants to use deformable_DETR. Regardless, any advice would be welcome (:

daimakram commented 2 years ago

@jackroos, @krxxxxxxxanc, Can you please address this issue? Which parts of the model should we remove to accommodate fine-tuning? Will highly appreciate your response.

daimakram commented 2 years ago

Hi @aviadlazar, were you able to resolve it?

aviadlazar commented 2 years ago

@daimakram sadly I couldn't resolve it.