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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: 0.6402 |
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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: 15 |
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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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| 5.2965 | 0.19 | 50 | 4.4784 | |
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| 4.7649 | 0.38 | 100 | 4.3439 | |
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| 4.4907 | 0.57 | 150 | 4.0077 | |
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| 4.3973 | 0.76 | 200 | 3.2143 | |
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| 3.4084 | 0.95 | 250 | 2.6818 | |
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| 2.7091 | 1.14 | 300 | 2.3603 | |
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| 2.4601 | 1.33 | 350 | 1.9004 | |
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| 2.1096 | 1.52 | 400 | 1.5639 | |
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| 1.6941 | 1.7 | 450 | 1.3240 | |
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| 1.4949 | 1.89 | 500 | 1.1247 | |
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| 1.2246 | 2.08 | 550 | 1.0421 | |
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| 1.4479 | 2.27 | 600 | 1.1546 | |
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| 1.1327 | 2.46 | 650 | 1.1098 | |
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| 1.1184 | 2.65 | 700 | 0.8950 | |
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| 1.0516 | 2.84 | 750 | 0.8601 | |
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| 1.2556 | 3.03 | 800 | 0.8575 | |
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| 1.1216 | 3.22 | 850 | 0.8314 | |
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| 1.1027 | 3.41 | 900 | 1.0676 | |
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| 1.0815 | 3.6 | 950 | 0.9716 | |
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| 1.2254 | 3.79 | 1000 | 1.0091 | |
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| 0.9896 | 3.98 | 1050 | 0.7600 | |
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| 1.0736 | 4.17 | 1100 | 0.8907 | |
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| 1.2462 | 4.36 | 1150 | 0.7506 | |
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| 0.9959 | 4.55 | 1200 | 0.7623 | |
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| 1.0895 | 4.73 | 1250 | 0.7570 | |
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| 1.0736 | 4.92 | 1300 | 0.8248 | |
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| 1.1015 | 5.11 | 1350 | 0.8682 | |
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| 1.1423 | 5.3 | 1400 | 0.8340 | |
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| 1.0906 | 5.49 | 1450 | 0.8372 | |
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| 0.9333 | 5.68 | 1500 | 0.8420 | |
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| 1.1347 | 5.87 | 1550 | 0.8718 | |
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| 0.9407 | 6.06 | 1600 | 0.8270 | |
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| 0.8138 | 6.25 | 1650 | 0.8241 | |
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| 0.8731 | 6.44 | 1700 | 0.8013 | |
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| 1.0146 | 6.63 | 1750 | 0.7704 | |
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| 0.8847 | 6.82 | 1800 | 0.8885 | |
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| 1.0283 | 7.01 | 1850 | 0.8804 | |
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| 1.0359 | 7.2 | 1900 | 0.7907 | |
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| 0.987 | 7.39 | 1950 | 0.7997 | |
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| 1.0279 | 7.58 | 2000 | 0.9095 | |
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| 0.9027 | 7.77 | 2050 | 0.6823 | |
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| 0.927 | 7.95 | 2100 | 0.6728 | |
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| 1.0499 | 8.14 | 2150 | 0.6537 | |
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| 0.9774 | 8.33 | 2200 | 0.6455 | |
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| 0.9171 | 8.52 | 2250 | 0.6456 | |
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| 1.0002 | 8.71 | 2300 | 0.6723 | |
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| 0.9052 | 8.9 | 2350 | 0.6554 | |
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| 0.9029 | 9.09 | 2400 | 0.7272 | |
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| 1.0247 | 9.28 | 2450 | 0.6997 | |
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| 0.8296 | 9.47 | 2500 | 0.6661 | |
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| 1.0659 | 9.66 | 2550 | 0.7914 | |
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| 1.0226 | 9.85 | 2600 | 0.7823 | |
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| 0.9419 | 10.04 | 2650 | 0.7709 | |
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| 0.9008 | 10.23 | 2700 | 0.8114 | |
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| 0.826 | 10.42 | 2750 | 0.7042 | |
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| 0.7957 | 10.61 | 2800 | 0.7764 | |
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| 1.0086 | 10.8 | 2850 | 0.8362 | |
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| 1.0076 | 10.98 | 2900 | 0.8048 | |
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| 0.9613 | 11.17 | 2950 | 0.6945 | |
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| 0.9155 | 11.36 | 3000 | 0.7011 | |
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| 0.9436 | 11.55 | 3050 | 0.6524 | |
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| 0.9134 | 11.74 | 3100 | 0.6582 | |
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| 0.817 | 11.93 | 3150 | 0.6678 | |
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| 0.8545 | 12.12 | 3200 | 0.6520 | |
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| 0.9801 | 12.31 | 3250 | 0.7813 | |
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| 0.8566 | 12.5 | 3300 | 0.7205 | |
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| 0.8966 | 12.69 | 3350 | 0.6326 | |
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| 0.8705 | 12.88 | 3400 | 0.6577 | |
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| 0.8193 | 13.07 | 3450 | 0.6391 | |
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| 0.8099 | 13.26 | 3500 | 0.6658 | |
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| 0.921 | 13.45 | 3550 | 0.6535 | |
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| 0.7915 | 13.64 | 3600 | 0.6576 | |
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| 1.1439 | 13.83 | 3650 | 0.6593 | |
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| 0.8702 | 14.02 | 3700 | 0.6519 | |
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| 0.73 | 14.2 | 3750 | 0.6403 | |
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| 0.8306 | 14.39 | 3800 | 0.6393 | |
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| 0.8678 | 14.58 | 3850 | 0.6405 | |
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| 1.0003 | 14.77 | 3900 | 0.6407 | |
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| 1.023 | 14.96 | 3950 | 0.6402 | |
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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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