Instructions to use ustc-community/dfine-xlarge-coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ustc-community/dfine-xlarge-coco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ustc-community/dfine-xlarge-coco")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("ustc-community/dfine-xlarge-coco") model = AutoModelForObjectDetection.from_pretrained("ustc-community/dfine-xlarge-coco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add model from convert_d_fine_original_pytorch_checkpoint_to_hf.py
Browse files- config.json +4 -1
- model.safetensors +2 -2
config.json
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{
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"_attn_implementation_autoset": true,
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"activation_dropout": 0.0,
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"activation_function": "silu",
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"anchor_image_size": [
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640,
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640
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],
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"attention_dropout": 0.0,
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"auxiliary_loss": true,
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"backbone": null,
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"positional_encoding_temperature": 10000,
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"reg_scale": 4.0,
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"top_prob_values": 4,
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"transformers_version": "4.50.0.dev0",
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"use_focal_loss": true,
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"use_pretrained_backbone": false,
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{
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"activation_dropout": 0.0,
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"activation_function": "silu",
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"anchor_image_size": [
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640,
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640
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],
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"architectures": [
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"DFineForObjectDetection"
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],
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"attention_dropout": 0.0,
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"auxiliary_loss": true,
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"backbone": null,
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"positional_encoding_temperature": 10000,
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"reg_scale": 4.0,
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"top_prob_values": 4,
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"torch_dtype": "float32",
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"transformers_version": "4.50.0.dev0",
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"use_focal_loss": true,
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"use_pretrained_backbone": false,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:8a623cb5f6a31d98ccdd843ad1c9d58f8fbd0ba7c30a11faa51abe9b783e57ae
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size 251616600
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