Instructions to use susnato/detr-resnet-50_finetuned_plant_disease_detection_processed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use susnato/detr-resnet-50_finetuned_plant_disease_detection_processed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="susnato/detr-resnet-50_finetuned_plant_disease_detection_processed")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("susnato/detr-resnet-50_finetuned_plant_disease_detection_processed") model = AutoModelForObjectDetection.from_pretrained("susnato/detr-resnet-50_finetuned_plant_disease_detection_processed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "facebook/detr-resnet-50", | |
| "activation_dropout": 0.0, | |
| "activation_function": "relu", | |
| "architectures": [ | |
| "DetrForObjectDetection" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auxiliary_loss": false, | |
| "backbone": "resnet50", | |
| "backbone_config": null, | |
| "bbox_cost": 5, | |
| "bbox_loss_coefficient": 5, | |
| "class_cost": 1, | |
| "classifier_dropout": 0.0, | |
| "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, | |
| "eos_coefficient": 0.1, | |
| "giou_cost": 2, | |
| "giou_loss_coefficient": 2, | |
| "id2label": { | |
| "0": "Cherry leaf", | |
| "1": "Peach leaf", | |
| "2": "Corn leaf blight", | |
| "3": "Apple rust leaf", | |
| "4": "Potato leaf late blight", | |
| "5": "Strawberry leaf", | |
| "6": "Tomato leaf late blight", | |
| "7": "Tomato mold leaf", | |
| "8": "Potato leaf early blight", | |
| "9": "Apple leaf", | |
| "10": "Tomato leaf yellow virus", | |
| "11": "Blueberry leaf", | |
| "12": "Tomato leaf mosaic virus", | |
| "13": "Raspberry leaf", | |
| "14": "Tomato leaf bacterial spot", | |
| "15": "Squash Powdery mildew leaf", | |
| "16": "grape leaf", | |
| "17": "Tomato Early blight leaf", | |
| "18": "Apple Scab Leaf", | |
| "19": "Tomato Septoria leaf spot", | |
| "20": "Tomato leaf", | |
| "21": "Soyabean leaf", | |
| "22": "Bell_pepper leaf spot", | |
| "23": "Bell_pepper leaf" | |
| }, | |
| "init_std": 0.02, | |
| "init_xavier_std": 1.0, | |
| "is_encoder_decoder": true, | |
| "label2id": { | |
| "Apple Scab Leaf": 18, | |
| "Apple leaf": 9, | |
| "Apple rust leaf": 3, | |
| "Bell_pepper leaf": 23, | |
| "Bell_pepper leaf spot": 22, | |
| "Blueberry leaf": 11, | |
| "Cherry leaf": 0, | |
| "Corn leaf blight": 2, | |
| "Peach leaf": 1, | |
| "Potato leaf early blight": 8, | |
| "Potato leaf late blight": 4, | |
| "Raspberry leaf": 13, | |
| "Soyabean leaf": 21, | |
| "Squash Powdery mildew leaf": 15, | |
| "Strawberry leaf": 5, | |
| "Tomato Early blight leaf": 17, | |
| "Tomato Septoria leaf spot": 19, | |
| "Tomato leaf": 20, | |
| "Tomato leaf bacterial spot": 14, | |
| "Tomato leaf late blight": 6, | |
| "Tomato leaf mosaic virus": 12, | |
| "Tomato leaf yellow virus": 10, | |
| "Tomato mold leaf": 7, | |
| "grape leaf": 16 | |
| }, | |
| "mask_loss_coefficient": 1, | |
| "max_position_embeddings": 1024, | |
| "model_type": "detr", | |
| "num_channels": 3, | |
| "num_hidden_layers": 6, | |
| "num_queries": 100, | |
| "position_embedding_type": "sine", | |
| "scale_embedding": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.0.dev0", | |
| "use_pretrained_backbone": true, | |
| "use_timm_backbone": true | |
| } | |