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# plant-seedlings-resnet-152

This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2604
- Accuracy: 0.9147

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.175         | 0.53  | 100  | 2.1135          | 0.3247   |
| 1.146         | 1.06  | 200  | 1.0761          | 0.6654   |
| 0.8299        | 1.6   | 300  | 0.7586          | 0.7391   |
| 0.7896        | 2.13  | 400  | 0.7093          | 0.7680   |
| 0.7327        | 2.66  | 500  | 0.5140          | 0.8207   |
| 0.5207        | 3.19  | 600  | 0.5375          | 0.8183   |
| 0.6465        | 3.72  | 700  | 0.4620          | 0.8465   |
| 0.2745        | 4.26  | 800  | 0.4784          | 0.8324   |
| 0.5366        | 4.79  | 900  | 0.4804          | 0.8355   |
| 0.4467        | 5.32  | 1000 | 0.4354          | 0.8551   |
| 0.3604        | 5.85  | 1100 | 0.3950          | 0.8680   |
| 0.2511        | 6.38  | 1200 | 0.4279          | 0.8594   |
| 0.326         | 6.91  | 1300 | 0.3677          | 0.8852   |
| 0.3444        | 7.45  | 1400 | 0.3539          | 0.8748   |
| 0.4015        | 7.98  | 1500 | 0.3161          | 0.8950   |
| 0.2821        | 8.51  | 1600 | 0.4394          | 0.8686   |
| 0.435         | 9.04  | 1700 | 0.3408          | 0.8920   |
| 0.3318        | 9.57  | 1800 | 0.3886          | 0.8778   |
| 0.2441        | 10.11 | 1900 | 0.2854          | 0.9042   |
| 0.2467        | 10.64 | 2000 | 0.3248          | 0.8883   |
| 0.2082        | 11.17 | 2100 | 0.3080          | 0.8956   |
| 0.1983        | 11.7  | 2200 | 0.3394          | 0.8963   |
| 0.2609        | 12.23 | 2300 | 0.3582          | 0.8870   |
| 0.2055        | 12.77 | 2400 | 0.3330          | 0.8963   |
| 0.3476        | 13.3  | 2500 | 0.2852          | 0.9091   |
| 0.223         | 13.83 | 2600 | 0.3115          | 0.8999   |
| 0.2307        | 14.36 | 2700 | 0.2986          | 0.9098   |
| 0.3113        | 14.89 | 2800 | 0.3103          | 0.8993   |
| 0.1792        | 15.43 | 2900 | 0.2862          | 0.9098   |
| 0.1685        | 15.96 | 3000 | 0.2935          | 0.9055   |
| 0.2429        | 16.49 | 3100 | 0.2882          | 0.9122   |
| 0.2548        | 17.02 | 3200 | 0.2748          | 0.9165   |
| 0.3561        | 17.55 | 3300 | 0.2684          | 0.9171   |
| 0.1982        | 18.09 | 3400 | 0.2647          | 0.9147   |
| 0.1638        | 18.62 | 3500 | 0.2509          | 0.9171   |
| 0.2404        | 19.15 | 3600 | 0.2936          | 0.9165   |
| 0.2424        | 19.68 | 3700 | 0.2604          | 0.9147   |


### 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-152", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9146715776550031, "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-152
==========================


This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2604
* Accuracy: 0.9147


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: 20
* 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: 20
* 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: 20
* 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: 20
* 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