
# Model Trained Using AutoTrain

- Problem type: Image Classification
- CO2 Emissions (in grams): 0.0000

## Validation Metricsg
loss: 0.725390613079071

f1: 0.6666666666666666

precision: 0.5

recall: 1.0

auc: 0.8

accuracy: 0.5

{"tags": ["autotrain", "image-classification"], "datasets": ["ameerazam08/autotrain-data-docker-check-1"], "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": 0}}
transformers pytorch swin image-classification autotrain dataset:ameerazam08/autotrain-data-docker-check-1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #swin #image-classification #autotrain #dataset-ameerazam08/autotrain-data-docker-check-1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Image Classification
- CO2 Emissions (in grams): 0.0000

## Validation Metricsg
loss: 0.725390613079071

f1: 0.6666666666666666

precision: 0.5

recall: 1.0

auc: 0.8

accuracy: 0.5

# Model Trained Using AutoTrain

- Problem type: Image Classification
- CO2 Emissions (in grams): 0.0000 ## Validation Metricsg
loss: 0.725390613079071

f1: 0.6666666666666666

precision: 0.5

recall: 1.0

auc: 0.8

accuracy: 0.5
TAGS
#transformers #pytorch #swin #image-classification #autotrain #dataset-ameerazam08/autotrain-data-docker-check-1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Image Classification
- CO2 Emissions (in grams): 0.0000 ## Validation Metricsg
loss: 0.725390613079071

f1: 0.6666666666666666

precision: 0.5

recall: 1.0

auc: 0.8

accuracy: 0.5