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# plant-seedlings-model-ResNet18-freeze-0-12-20ep

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2060
- Accuracy: 0.9327

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4528        | 0.25  | 128   | 0.6686          | 0.7878   |
| 0.4291        | 0.5   | 256   | 0.6259          | 0.7903   |
| 0.4961        | 0.75  | 384   | 0.5677          | 0.8055   |
| 0.4637        | 1.01  | 512   | 0.5073          | 0.8330   |
| 0.6897        | 1.26  | 640   | 0.5817          | 0.8060   |
| 0.5257        | 1.51  | 768   | 0.5118          | 0.8276   |
| 0.5381        | 1.76  | 896   | 0.4809          | 0.8384   |
| 0.4736        | 2.01  | 1024  | 0.3976          | 0.8595   |
| 0.4967        | 2.26  | 1152  | 0.4192          | 0.8566   |
| 0.4505        | 2.51  | 1280  | 0.4128          | 0.8590   |
| 0.4211        | 2.77  | 1408  | 0.4075          | 0.8576   |
| 0.3877        | 3.02  | 1536  | 0.3796          | 0.8738   |
| 0.3134        | 3.27  | 1664  | 0.3906          | 0.8762   |
| 0.3596        | 3.52  | 1792  | 0.3703          | 0.8846   |
| 0.3859        | 3.77  | 1920  | 0.3125          | 0.8954   |
| 0.4076        | 4.02  | 2048  | 0.3718          | 0.8615   |
| 0.3109        | 4.28  | 2176  | 0.3449          | 0.8924   |
| 0.4588        | 4.53  | 2304  | 0.3377          | 0.8875   |
| 0.2923        | 4.78  | 2432  | 0.3001          | 0.8998   |
| 0.3273        | 5.03  | 2560  | 0.3187          | 0.8880   |
| 0.2541        | 5.28  | 2688  | 0.3432          | 0.8856   |
| 0.3059        | 5.53  | 2816  | 0.3236          | 0.8988   |
| 0.2979        | 5.78  | 2944  | 0.3532          | 0.8851   |
| 0.2748        | 6.04  | 3072  | 0.3407          | 0.8885   |
| 0.3537        | 6.29  | 3200  | 0.2925          | 0.8988   |
| 0.3364        | 6.54  | 3328  | 0.3071          | 0.9047   |
| 0.2135        | 6.79  | 3456  | 0.2765          | 0.9077   |
| 0.2023        | 7.04  | 3584  | 0.2919          | 0.9037   |
| 0.1977        | 7.29  | 3712  | 0.2812          | 0.8978   |
| 0.4042        | 7.54  | 3840  | 0.2954          | 0.8998   |
| 0.3662        | 7.8   | 3968  | 0.2857          | 0.9018   |
| 0.1872        | 8.05  | 4096  | 0.2504          | 0.9140   |
| 0.3959        | 8.3   | 4224  | 0.2984          | 0.8993   |
| 0.2403        | 8.55  | 4352  | 0.2847          | 0.8998   |
| 0.3689        | 8.8   | 4480  | 0.2872          | 0.9023   |
| 0.2819        | 9.05  | 4608  | 0.3104          | 0.9008   |
| 0.1926        | 9.3   | 4736  | 0.2871          | 0.8969   |
| 0.2371        | 9.56  | 4864  | 0.2733          | 0.9082   |
| 0.2566        | 9.81  | 4992  | 0.2816          | 0.9101   |
| 0.2174        | 10.06 | 5120  | 0.2719          | 0.9160   |
| 0.2359        | 10.31 | 5248  | 0.2497          | 0.9175   |
| 0.2986        | 10.56 | 5376  | 0.2847          | 0.9096   |
| 0.2239        | 10.81 | 5504  | 0.2493          | 0.9180   |
| 0.2132        | 11.06 | 5632  | 0.2567          | 0.9121   |
| 0.1934        | 11.32 | 5760  | 0.2722          | 0.9028   |
| 0.2026        | 11.57 | 5888  | 0.2456          | 0.9229   |
| 0.2457        | 11.82 | 6016  | 0.2483          | 0.9234   |
| 0.2537        | 12.07 | 6144  | 0.2409          | 0.9165   |
| 0.193         | 12.32 | 6272  | 0.2215          | 0.9239   |
| 0.1738        | 12.57 | 6400  | 0.2421          | 0.9165   |
| 0.2925        | 12.83 | 6528  | 0.2499          | 0.9150   |
| 0.1173        | 13.08 | 6656  | 0.2174          | 0.9258   |
| 0.2147        | 13.33 | 6784  | 0.2917          | 0.9131   |
| 0.1581        | 13.58 | 6912  | 0.2734          | 0.9175   |
| 0.1349        | 13.83 | 7040  | 0.2485          | 0.9165   |
| 0.1212        | 14.08 | 7168  | 0.2247          | 0.9268   |
| 0.2178        | 14.33 | 7296  | 0.2289          | 0.9268   |
| 0.0879        | 14.59 | 7424  | 0.2512          | 0.9219   |
| 0.2006        | 14.84 | 7552  | 0.2321          | 0.9293   |
| 0.2308        | 15.09 | 7680  | 0.2491          | 0.9263   |
| 0.2137        | 15.34 | 7808  | 0.2270          | 0.9312   |
| 0.1112        | 15.59 | 7936  | 0.2205          | 0.9249   |
| 0.1477        | 15.84 | 8064  | 0.2328          | 0.9307   |
| 0.1794        | 16.09 | 8192  | 0.2051          | 0.9332   |
| 0.0596        | 16.35 | 8320  | 0.2234          | 0.9347   |
| 0.0533        | 16.6  | 8448  | 0.2469          | 0.9293   |
| 0.1096        | 16.85 | 8576  | 0.1871          | 0.9401   |
| 0.1117        | 17.1  | 8704  | 0.2302          | 0.9249   |
| 0.1349        | 17.35 | 8832  | 0.2084          | 0.9391   |
| 0.1031        | 17.6  | 8960  | 0.2200          | 0.9283   |
| 0.2428        | 17.85 | 9088  | 0.2201          | 0.9298   |
| 0.1283        | 18.11 | 9216  | 0.2293          | 0.9273   |
| 0.1688        | 18.36 | 9344  | 0.2120          | 0.9307   |
| 0.0877        | 18.61 | 9472  | 0.2200          | 0.9229   |
| 0.1508        | 18.86 | 9600  | 0.2204          | 0.9327   |
| 0.0868        | 19.11 | 9728  | 0.2224          | 0.9293   |
| 0.211         | 19.36 | 9856  | 0.1988          | 0.9401   |
| 0.1059        | 19.61 | 9984  | 0.2082          | 0.9322   |
| 0.182         | 19.87 | 10112 | 0.2060          | 0.9327   |


### 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-model-ResNet18-freeze-0-12-20ep", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9327111984282908, "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-model-ResNet18-freeze-0-12-20ep
===============================================


This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2060
* Accuracy: 0.9327


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