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# plant-seedlings-resnet-50

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4375
- Accuracy: 0.8492

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3644        | 0.53  | 100  | 2.3284          | 0.2294   |
| 1.8235        | 1.06  | 200  | 1.8020          | 0.4396   |
| 1.4216        | 1.6   | 300  | 1.2533          | 0.6341   |
| 1.1626        | 2.13  | 400  | 0.8825          | 0.7186   |
| 0.9175        | 2.66  | 500  | 0.7782          | 0.7598   |
| 0.819         | 3.19  | 600  | 0.6772          | 0.7814   |
| 0.7846        | 3.72  | 700  | 0.6027          | 0.8006   |
| 0.6403        | 4.26  | 800  | 0.5450          | 0.8148   |
| 0.6702        | 4.79  | 900  | 0.5621          | 0.8119   |
| 0.6038        | 5.32  | 1000 | 0.4854          | 0.8374   |
| 0.7048        | 5.85  | 1100 | 0.4793          | 0.8325   |
| 0.4157        | 6.38  | 1200 | 0.4724          | 0.8345   |
| 0.4833        | 6.91  | 1300 | 0.4614          | 0.8404   |
| 0.5064        | 7.45  | 1400 | 0.4901          | 0.8340   |
| 0.4966        | 7.98  | 1500 | 0.4365          | 0.8522   |
| 0.5718        | 8.51  | 1600 | 0.4375          | 0.8423   |
| 0.41          | 9.04  | 1700 | 0.4322          | 0.8536   |
| 0.4956        | 9.57  | 1800 | 0.4375          | 0.8492   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "plant-seedlings-resnet-50", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8492141453831041, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

plant-seedlings-resnet-50
=========================


This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4375
* Accuracy: 0.8492


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: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3