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
End of training
Browse files
README.md
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---
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license: apache-2.0
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base_model: facebook/detr-resnet-50
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tags:
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- generated_from_trainer
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model-index:
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- name: detr-resnet-50_finetuned_plant_disease_detection_processed
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# detr-resnet-50_finetuned_plant_disease_detection_processed
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This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9493
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.2
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- num_epochs: 2
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 5.9422 | 0.19 | 50 | 5.2172 |
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| 4.7005 | 0.38 | 100 | 3.7455 |
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| 3.1157 | 0.57 | 150 | 2.5489 |
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| 2.0862 | 0.76 | 200 | 1.4877 |
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| 1.6446 | 0.95 | 250 | 1.1362 |
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| 1.3925 | 1.14 | 300 | 1.0643 |
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| 1.3248 | 1.33 | 350 | 1.0942 |
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| 1.3461 | 1.52 | 400 | 1.0313 |
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| 1.269 | 1.7 | 450 | 1.0442 |
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| 1.227 | 1.89 | 500 | 0.9493 |
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### Framework versions
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- Transformers 4.36.0.dev0
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- Pytorch 2.0.1+cu117
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- Datasets 2.14.5
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- Tokenizers 0.15.0
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runs/Nov25_20-06-48_susnato-desktop/events.out.tfevents.1700923010.susnato-desktop.8082.0
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version https://git-lfs.github.com/spec/v1
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size 11053
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