sanali209/imclasif-genres-v001

README.md

@@ -1,47 +1,60 @@
 ---
 tags:
-- image-classification
-- pytorch
-- huggingpics
-metrics:
-- accuracy
-
+- generated_from_trainer
+datasets:
+- imagefolder
 model-index:
-- name: sanali209/imclasif-genres-v001
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    metrics:
-      - name: Accuracy
-        type: accuracy
-        value: 0.8783348202705383
+- name: imclasif-genres-v001
+  results: []
 ---
 
-# sanali209/imclasif-genres-v001
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# imclasif-genres-v001
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0340
 
+## Model description
 
-Autogenerated by HuggingPics🤗🖼️
+More information needed
 
-Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
+## Intended uses & limitations
 
-Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
+More information needed
 
+## Training and evaluation data
 
-## Example Images
+More information needed
 
-## labels
+## Training procedure
 
-3d render
+### Training hyperparameters
 
-photo
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
 
-combined
+### Training results
 
-drawing
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 54   | 1.2259          |
+| No log        | 2.0   | 108  | 1.0907          |
+| No log        | 3.0   | 162  | 1.0382          |
+| No log        | 4.0   | 216  | 1.0340          |
 
-pixel art
 
-text
+### Framework versions
 
-other
+- Transformers 4.44.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "google/vit-base-patch16-224-in21k",
+  "_name_or_path": "sanali209/imclasif-genres-v001",
   "architectures": [
     "ViTForImageClassification"
   ],
@@ -9,7 +9,7 @@
   "hidden_dropout_prob": 0.0,
   "hidden_size": 768,
   "id2label": {
-    "0": "3d render",
+    "0": "3d renderer",
     "1": "combined",
     "2": "drawing",
     "3": "other",
@@ -21,13 +21,13 @@
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "3d render": "0",
-    "combined": "1",
-    "drawing": "2",
-    "other": "3",
-    "photo": "4",
-    "pixel art": "5",
-    "text": "6"
+    "3d renderer": 0,
+    "combined": 1,
+    "drawing": 2,
+    "other": 3,
+    "photo": 4,
+    "pixel art": 5,
+    "text": 6
   },
   "layer_norm_eps": 1e-12,
   "model_type": "vit",
@@ -38,5 +38,5 @@
   "problem_type": "single_label_classification",
   "qkv_bias": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.37.2"
+  "transformers_version": "4.44.0"
 }

model.safetensors

@@ -1,3 +1,3 @@
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 size 343239356

preprocessor_config.json

@@ -7,7 +7,7 @@
     0.5,
     0.5
   ],
-  "image_processor_type": "ViTFeatureExtractor",
+  "image_processor_type": "ViTImageProcessor",
   "image_std": [
     0.5,
     0.5,

training_args.bin

@@ -0,0 +1,3 @@
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+oid sha256:ba8806f1f99d2a0b91af3490ef0a113f0a85f281ca0b04a2d98f6ee20c7c91f7
+size 5176