# AutoTrain Dataset for project: soft-tissue-tumor-species

## Dataset Description

This dataset has been automatically processed by AutoTrain for project bone-tumor-species.

### Languages

The BCP-47 code for the dataset's language is unk.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "image": "<512x512 RGB PIL image>",
    "target": 16
  },
  {
    "image": "<512x512 RGB PIL image>",
    "target": 29
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "image": "Image(decode=True, id=None)",
  "target": "ClassLabel(names=['Adipose Tissue', 'Alveolar Rhabdomyosarcoma', 'Alveolar Soft Part Sarcoma', 'Angioleiomyoma', 'Angiosarcoma', 'Clear Cell Sarcoma', 'Dedifferentiated Liposarcoma', 'Dense Connective Tissue', 'Dermatofibrosarcoma Protuberans', 'Desmoplastic Small Round Cell Tumor', 'Elastic Connective Tissue', 'Elastofibroma', 'Embryonal Rhabdomyosarcoma', 'Epithelioid Hemangioendothelioma', 'Epithelioid Sarcoma', 'Extraskeletal Myxoid Chondrosarcoma', 'Fibrocartilage', 'Fibroma (of Tendon Sheath)', 'Fibromatosis', 'Fibrosarcoma', 'Fibrous Histiocytoma', 'Glomus Tumor', 'Granular Cell Tumor', 'Hemangioma', 'Heterotopic Ossification (Myositis Ossificans)', 'Hibernoma', 'Hyaline Cartilage', 'Inflammatory Myofibroblastic Tumor', 'Kaposi Sarcoma', 'Leiomyosarcoma', 'Lipoblastoma', 'Lipoma', 'Loose Connective Tissue', 'Low Grade Fibromyxoid Sarcoma', 'Malignant Peripheral Nerve Sheath Tumor', 'Myopericytoma', 'Myxofibrosarcoma', 'Myxoid Liposarcoma', 'Neurofibroma', 'Nodular Fasciitis', 'Perineurioma', 'Proliferative Fasciitis', 'Rhabdomyoma', 'Schwannoma', 'Sclerosing Epithelioid Fibrosarcoma', 'Skeletal Muscle', 'Solitary Fibrous Tumor', 'Spindle Cell Lipoma', 'Synovial Sarcoma', 'Tenosynovial Giant Cell Tumor', 'Tumoral Calcinosis', 'Undifferentiated Pleiomorphic Sarcoma'], id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 6268 |
| valid        | 1570 |

task_categories:image-classification region:us
{"task_categories": ["image-classification"]}
TAGS
#task_categories-image-classification #region-us 

AutoTrain Dataset for project: soft-tissue-tumor-species
========================================================


Dataset Description
-------------------


This dataset has been automatically processed by AutoTrain for project bone-tumor-species.


### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
-----------------


### Data Instances


A sample from this dataset looks as follows:


### Dataset Fields


The dataset has the following fields (also called "features"):


### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:




### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
TAGS
#task_categories-image-classification #region-us 
 ### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
passage: TAGS
#task_categories-image-classification #region-us 
### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
-----------------### Data Instances


A sample from this dataset looks as follows:### Dataset Fields


The dataset has the following fields (also called "features"):### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow: