---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# detr-resnet-50_finetuned_plant_disease_detection_processed

This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6402

## 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: 15
- mixed_precision_training: Native AMP

### Training results

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


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0