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
metadata
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50_finetuned_plant_disease_detection_processed
results: []
detr-resnet-50_finetuned_plant_disease_detection_processed
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9493
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.9422 | 0.19 | 50 | 5.2172 |
| 4.7005 | 0.38 | 100 | 3.7455 |
| 3.1157 | 0.57 | 150 | 2.5489 |
| 2.0862 | 0.76 | 200 | 1.4877 |
| 1.6446 | 0.95 | 250 | 1.1362 |
| 1.3925 | 1.14 | 300 | 1.0643 |
| 1.3248 | 1.33 | 350 | 1.0942 |
| 1.3461 | 1.52 | 400 | 1.0313 |
| 1.269 | 1.7 | 450 | 1.0442 |
| 1.227 | 1.89 | 500 | 0.9493 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0