
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3739699408
- CO2 Emissions (in grams): 18.1013

## Validation Metrics

- Loss: 0.347
- Accuracy: 0.890
- Macro F1: 0.896
- Micro F1: 0.890
- Weighted F1: 0.889
- Macro Precision: 0.902
- Micro Precision: 0.890
- Weighted Precision: 0.891
- Macro Recall: 0.891
- Micro Recall: 0.890
- Weighted Recall: 0.890
{"tags": ["autotrain", "vision", "image-classification"], "datasets": ["dazzle-nu/autotrain-data-weather-classification"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}], "co2_eq_emissions": {"emissions": 18.10129353152818}}
transformers pytorch resnet image-classification autotrain vision dataset:dazzle-nu/autotrain-data-weather-classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #resnet #image-classification #autotrain #vision #dataset-dazzle-nu/autotrain-data-weather-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3739699408
- CO2 Emissions (in grams): 18.1013

## Validation Metrics

- Loss: 0.347
- Accuracy: 0.890
- Macro F1: 0.896
- Micro F1: 0.890
- Weighted F1: 0.889
- Macro Precision: 0.902
- Micro Precision: 0.890
- Weighted Precision: 0.891
- Macro Recall: 0.891
- Micro Recall: 0.890
- Weighted Recall: 0.890
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3739699408
- CO2 Emissions (in grams): 18.1013 ## Validation Metrics

- Loss: 0.347
- Accuracy: 0.890
- Macro F1: 0.896
- Micro F1: 0.890
- Weighted F1: 0.889
- Macro Precision: 0.902
- Micro Precision: 0.890
- Weighted Precision: 0.891
- Macro Recall: 0.891
- Micro Recall: 0.890
- Weighted Recall: 0.890
TAGS
#transformers #pytorch #resnet #image-classification #autotrain #vision #dataset-dazzle-nu/autotrain-data-weather-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3739699408
- CO2 Emissions (in grams): 18.1013 ## Validation Metrics

- Loss: 0.347
- Accuracy: 0.890
- Macro F1: 0.896
- Micro F1: 0.890
- Weighted F1: 0.889
- Macro Precision: 0.902
- Micro Precision: 0.890
- Weighted Precision: 0.891
- Macro Recall: 0.891
- Micro Recall: 0.890
- Weighted Recall: 0.890
TAGS
#transformers #pytorch #resnet #image-classification #autotrain #vision #dataset-dazzle-nu/autotrain-data-weather-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3739699408
- CO2 Emissions (in grams): 18.1013## Validation Metrics

- Loss: 0.347
- Accuracy: 0.890
- Macro F1: 0.896
- Micro F1: 0.890
- Weighted F1: 0.889
- Macro Precision: 0.902
- Micro Precision: 0.890
- Weighted Precision: 0.891
- Macro Recall: 0.891
- Micro Recall: 0.890
- Weighted Recall: 0.890