Instructions to use jmgrodriguez/main_hf_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmgrodriguez/main_hf_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jmgrodriguez/main_hf_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("jmgrodriguez/main_hf_cppe5") model = AutoModelForObjectDetection.from_pretrained("jmgrodriguez/main_hf_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,113 Bytes
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"_name_or_path": "microsoft/conditional-detr-resnet-50",
"activation_dropout": 0.0,
"activation_function": "relu",
"architectures": [
"ConditionalDetrForObjectDetection"
],
"attention_dropout": 0.0,
"auxiliary_loss": false,
"backbone": "resnet50",
"backbone_config": null,
"backbone_kwargs": {
"in_chans": 3,
"out_indices": [
1,
2,
3,
4
]
},
"bbox_cost": 5,
"bbox_loss_coefficient": 5,
"class_cost": 2,
"cls_loss_coefficient": 2,
"d_model": 256,
"decoder_attention_heads": 8,
"decoder_ffn_dim": 2048,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"dice_loss_coefficient": 1,
"dilation": false,
"dropout": 0.1,
"encoder_attention_heads": 8,
"encoder_ffn_dim": 2048,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"focal_alpha": 0.25,
"giou_cost": 2,
"giou_loss_coefficient": 2,
"id2label": {
"0": "Coverall",
"1": "Face_Shield",
"2": "Gloves",
"3": "Goggles",
"4": "Mask"
},
"init_std": 0.02,
"init_xavier_std": 1.0,
"is_encoder_decoder": true,
"label2id": {
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"airplane": 5,
"apple": 53,
"backpack": 27,
"banana": 52,
"baseball bat": 39,
"baseball glove": 40,
"bear": 23,
"bed": 65,
"bench": 15,
"bicycle": 2,
"bird": 16,
"boat": 9,
"book": 84,
"bottle": 44,
"bowl": 51,
"broccoli": 56,
"bus": 6,
"cake": 61,
"car": 3,
"carrot": 57,
"cat": 17,
"cell phone": 77,
"chair": 62,
"clock": 85,
"couch": 63,
"cow": 21,
"cup": 47,
"dining table": 67,
"dog": 18,
"donut": 60,
"elephant": 22,
"fire hydrant": 11,
"fork": 48,
"frisbee": 34,
"giraffe": 25,
"hair drier": 89,
"handbag": 31,
"horse": 19,
"hot dog": 58,
"keyboard": 76,
"kite": 38,
"knife": 49,
"laptop": 73,
"microwave": 78,
"motorcycle": 4,
"mouse": 74,
"orange": 55,
"oven": 79,
"parking meter": 14,
"person": 1,
"pizza": 59,
"potted plant": 64,
"refrigerator": 82,
"remote": 75,
"sandwich": 54,
"scissors": 87,
"sheep": 20,
"sink": 81,
"skateboard": 41,
"skis": 35,
"snowboard": 36,
"spoon": 50,
"sports ball": 37,
"stop sign": 13,
"suitcase": 33,
"surfboard": 42,
"teddy bear": 88,
"tennis racket": 43,
"tie": 32,
"toaster": 80,
"toilet": 70,
"toothbrush": 90,
"traffic light": 10,
"train": 7,
"truck": 8,
"tv": 72,
"umbrella": 28,
"vase": 86,
"wine glass": 46,
"zebra": 24
},
"mask_loss_coefficient": 1,
"max_position_embeddings": 1024,
"model_type": "conditional_detr",
"num_channels": 3,
"num_hidden_layers": 6,
"num_queries": 300,
"position_embedding_type": "sine",
"scale_embedding": false,
"torch_dtype": "float32",
"transformers_version": "4.42.4",
"use_pretrained_backbone": true,
"use_timm_backbone": true
}
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