commit files to HF hub

README.md

@@ -1,60 +1,31 @@
 ---
 tags:
-- generated_from_trainer
-datasets:
-- imagefolder
+- image-classification
+- pytorch
+- huggingpics
+metrics:
+- accuracy
+
 model-index:
-- name: imclasif-genres-v001
-  results: []
+- name: sanali209/imclasif-genres-v001
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.8868578672409058
 ---
 
-<!-- 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
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+# sanali209/imclasif-genres-v001
 
-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
 
-### Training results
+Autogenerated by HuggingPics🤗🖼️
 
-| 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          |
+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).
 
+Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
 
-### Framework versions
 
-- Transformers 4.44.0
-- Pytorch 2.3.1+cu121
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+## Example Images

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "sanali209/imclasif-genres-v001",
+  "_name_or_path": "google/vit-base-patch16-224",
   "architectures": [
     "ViTForImageClassification"
   ],
@@ -9,7 +9,7 @@
   "hidden_dropout_prob": 0.0,
   "hidden_size": 768,
   "id2label": {
-    "0": "3d renderer",
+    "0": "3d render",
     "1": "combined",
     "2": "drawing",
     "3": "other",
@@ -21,13 +21,13 @@
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "3d renderer": 0,
-    "combined": 1,
-    "drawing": 2,
-    "other": 3,
-    "photo": 4,
-    "pixel art": 5,
-    "text": 6
+    "3d render": "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": "4.44.0"
+  "transformers_version": "4.44.2"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:69ac4d625b25aa1a8ba203eb98bc93ebbf39817ad9d6a8731aea04b6c4d1bb9f
+oid sha256:f0eb214f123dc7f948df976691ef970519c0a3957962520ceba6a6e79f125d19
 size 343239356

preprocessor_config.json

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

runs/events.out.tfevents.1729920614.6934cbcb2c82.1901.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3550c215f4ff8006c51ccd058ea9a012ffa25cc8f1e1f6b86e40a9b7fab6495
+size 17584