
# Usage

```
from transformers import pipeline
p = pipeline("image-classification", model="juliensimon/autotrain-food101-1471154053")
result = p("my_image.jpg")
```

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154

## Validation Metrics

- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915
{"tags": ["autotrain", "vision", "image-classification"], "datasets": ["juliensimon/autotrain-data-food101"], "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": 179.11544810549532}}
transformers pytorch safetensors autotrain vision image-classification dataset:juliensimon/autotrain-data-food101 co2_eq_emissions endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #autotrain #vision #image-classification #dataset-juliensimon/autotrain-data-food101 #co2_eq_emissions #endpoints_compatible #region-us 


# Usage



# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154

## Validation Metrics

- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915
# Usage # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154 ## Validation Metrics

- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915
TAGS
#transformers #pytorch #safetensors #autotrain #vision #image-classification #dataset-juliensimon/autotrain-data-food101 #co2_eq_emissions #endpoints_compatible #region-us 
 # Usage # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154 ## Validation Metrics

- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915
passage: TAGS
#transformers #pytorch #safetensors #autotrain #vision #image-classification #dataset-juliensimon/autotrain-data-food101 #co2_eq_emissions #endpoints_compatible #region-us 
# Usage# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154## Validation Metrics

- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915