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# weeds_hfclass20
Model is trained on imbalanced dataset/ .8 .1 .1 split/ 224x224 resized

Dataset: https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset

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: 0.4375
- Accuracy: 0.8696

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.444         | 1.0   | 69   | 2.4226          | 0.2018   |
| 2.3378        | 2.0   | 138  | 2.2755          | 0.3268   |
| 1.9474        | 3.0   | 207  | 1.8114          | 0.5286   |
| 1.4306        | 4.0   | 276  | 1.2129          | 0.6571   |
| 0.9848        | 5.0   | 345  | 0.8457          | 0.7536   |
| 0.8489        | 6.0   | 414  | 0.6503          | 0.8      |
| 0.7054        | 7.0   | 483  | 0.5202          | 0.8411   |
| 0.6404        | 8.0   | 552  | 0.5067          | 0.8607   |
| 0.5939        | 9.0   | 621  | 0.4575          | 0.8589   |
| 0.6365        | 10.0  | 690  | 0.4375          | 0.8696   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "weeds_hfclass20", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8696428571428572, "name": "Accuracy"}]}]}]}
transformers pytorch resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

weeds\_hfclass20
================


Model is trained on imbalanced dataset/ .8 .1 .1 split/ 224x224 resized


Dataset: URL


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: 0.4375
* Accuracy: 0.8696


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2