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v-mdd-2000
eligapris/v-mdd-2000
v-mdd-2000 is a image classification model from eligapris. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- resnet
- Access
- Public
- Created
- Oct 18, 2024
- Updated
- Oct 18, 2024
- SHA
- f43cb718
Base models
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Image Classification
Image Classification Model Results (AutoTrain)
Validation Metrics
| Metric | Value |
|---|---|
| Loss | 0.5462 |
| Accuracy | 0.7371 |
F1 Scores
| Type | Value |
|---|---|
| Macro | 0.3900 |
| Micro | 0.7371 |
| Weighted | 0.6628 |
Precision
| Type | Value |
|---|---|
| Macro | 0.3468 |
| Micro | 0.7371 |
| Weighted | 0.6320 |
Recall
| Type | Value |
|---|---|
| Macro | 0.4972 |
| Micro | 0.7371 |
| Weighted | 0.7371 |
How to use
This model is designed for image classification. Here's how you can use it:
from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image
model_name = "eligapris/v-mdd-2000"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
image = Image.open("path_to_your_image.jpg")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
- Task
- Image Classification
- Library
- transformers
- Type
- resnet
- License
- apache-2.0
- Created
- Oct 18, 2024
- Updated
- Oct 18, 2024